0_PrepareforML.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "2a7f27b2-af10-4cc9-83b2-282d9adaf378",
   "metadata": {},
   "source": [
    "# Sentiment Analysis On Amazon Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c848fa-d202-4f27-b080-898cab52d965",
   "metadata": {},
   "source": [
    "![TM](source/textMining.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d35d1d4-765f-4fbc-a043-1cfdfe9d9941",
   "metadata": {},
   "source": [
    "## STEP1: Import Data From CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24629ba7-3004-4910-aef9-225ae9b96585",
   "metadata": {},
   "source": [
    "### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5719f13f-75a2-4776-9cf0-55d2032e736d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a94444fc-a29b-4861-93f5-73bb58b3158d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"source/reviews.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c1084a-a39f-4ab5-a475-c13a8f28fcdf",
   "metadata": {},
   "source": [
    "### First Look"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f058e907-ece4-468c-ab5d-9acb6447f399",
   "metadata": {},
   "source": [
    "- Aim of this step have pre-info about dataset\n",
    "- Start with a part of dataset to understand structure\n",
    "- Think about columns and rows\n",
    "- Learn which data types are using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1119d01b-15e5-44ca-a497-8440359bc2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568454, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d0f575-5328-48cf-813d-5adcbbf0a2e9",
   "metadata": {},
   "source": [
    "568454 rows, 10 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "080d2bfd-a5cf-43fe-8f36-29ccfb2104d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
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       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      5  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      4  1219017600   \n",
       "3                     3                       3      2  1307923200   \n",
       "4                     0                       0      5  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head(5)"
   ]
  },
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   "cell_type": "markdown",
   "id": "0f8d11dc-21cc-4c6b-8a0d-7124af211491",
   "metadata": {},
   "source": [
    "first 5 rows of df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6a7baa88-a251-4858-a532-8632cf3df48e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
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       "      <th>Text</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568449</th>\n",
       "      <td>568450</td>\n",
       "      <td>B001EO7N10</td>\n",
       "      <td>A28KG5XORO54AY</td>\n",
       "      <td>Lettie D. Carter</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1299628800</td>\n",
       "      <td>Will not do without</td>\n",
       "      <td>Great for sesame chicken..this is a good if no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568450</th>\n",
       "      <td>568451</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1331251200</td>\n",
       "      <td>disappointed</td>\n",
       "      <td>I'm disappointed with the flavor. The chocolat...</td>\n",
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       "    <tr>\n",
       "      <th>568451</th>\n",
       "      <td>568452</td>\n",
       "      <td>B004I613EE</td>\n",
       "      <td>A121AA1GQV751Z</td>\n",
       "      <td>pksd \"pk_007\"</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1329782400</td>\n",
       "      <td>Perfect for our maltipoo</td>\n",
       "      <td>These stars are small, so you can give 10-15 o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568452</th>\n",
       "      <td>568453</td>\n",
       "      <td>B004I613EE</td>\n",
       "      <td>A3IBEVCTXKNOH</td>\n",
       "      <td>Kathy A. Welch \"katwel\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1331596800</td>\n",
       "      <td>Favorite Training and reward treat</td>\n",
       "      <td>These are the BEST treats for training and rew...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568453</th>\n",
       "      <td>568454</td>\n",
       "      <td>B001LR2CU2</td>\n",
       "      <td>A3LGQPJCZVL9UC</td>\n",
       "      <td>srfell17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1338422400</td>\n",
       "      <td>Great Honey</td>\n",
       "      <td>I am very satisfied ,product is as advertised,...</td>\n",
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      "text/plain": [
       "            Id   ProductId          UserId              ProfileName  \\\n",
       "568449  568450  B001EO7N10  A28KG5XORO54AY         Lettie D. Carter   \n",
       "568450  568451  B003S1WTCU  A3I8AFVPEE8KI5                R. Sawyer   \n",
       "568451  568452  B004I613EE  A121AA1GQV751Z            pksd \"pk_007\"   \n",
       "568452  568453  B004I613EE   A3IBEVCTXKNOH  Kathy A. Welch \"katwel\"   \n",
       "568453  568454  B001LR2CU2  A3LGQPJCZVL9UC                 srfell17   \n",
       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "568449                     0                       0      5  1299628800   \n",
       "568450                     0                       0      2  1331251200   \n",
       "568451                     2                       2      5  1329782400   \n",
       "568452                     1                       1      5  1331596800   \n",
       "568453                     0                       0      5  1338422400   \n",
       "\n",
       "                                   Summary  \\\n",
       "568449                 Will not do without   \n",
       "568450                        disappointed   \n",
       "568451            Perfect for our maltipoo   \n",
       "568452  Favorite Training and reward treat   \n",
       "568453                         Great Honey   \n",
       "\n",
       "                                                     Text  \n",
       "568449  Great for sesame chicken..this is a good if no...  \n",
       "568450  I'm disappointed with the flavor. The chocolat...  \n",
       "568451  These stars are small, so you can give 10-15 o...  \n",
       "568452  These are the BEST treats for training and rew...  \n",
       "568453  I am very satisfied ,product is as advertised,...  "
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0a2f22c-a848-4022-a575-830ecfd68289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator',\n",
       "       'HelpfulnessDenominator', 'Score', 'Time', 'Summary', 'Text'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f728bb-b26e-40f6-b794-cda830633a83",
   "metadata": {},
   "source": [
    "all columns in df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b0040df-c215-46b4-8ce0-42a841e730f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                         int64\n",
       "ProductId                 object\n",
       "UserId                    object\n",
       "ProfileName               object\n",
       "HelpfulnessNumerator       int64\n",
       "HelpfulnessDenominator     int64\n",
       "Score                      int64\n",
       "Time                       int64\n",
       "Summary                   object\n",
       "Text                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad3cc52-e693-4e76-b4f5-0614167f0aa5",
   "metadata": {},
   "source": [
    "variables types of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6adb8f63-46be-4a35-a5ce-5fd7c87f4568",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>Id</th>\n",
       "      <td>568454.0</td>\n",
       "      <td>2.842275e+05</td>\n",
       "      <td>1.640987e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.421142e+05</td>\n",
       "      <td>2.842275e+05</td>\n",
       "      <td>4.263408e+05</td>\n",
       "      <td>5.684540e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <td>568454.0</td>\n",
       "      <td>1.743817e+00</td>\n",
       "      <td>7.636513e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>8.660000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <td>568454.0</td>\n",
       "      <td>2.228810e+00</td>\n",
       "      <td>8.289740e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.230000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>568454.0</td>\n",
       "      <td>4.183199e+00</td>\n",
       "      <td>1.310436e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>568454.0</td>\n",
       "      <td>1.296257e+09</td>\n",
       "      <td>4.804331e+07</td>\n",
       "      <td>939340800.0</td>\n",
       "      <td>1.271290e+09</td>\n",
       "      <td>1.311120e+09</td>\n",
       "      <td>1.332720e+09</td>\n",
       "      <td>1.351210e+09</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                           count          mean           std          min  \\\n",
       "Id                      568454.0  2.842275e+05  1.640987e+05          1.0   \n",
       "HelpfulnessNumerator    568454.0  1.743817e+00  7.636513e+00          0.0   \n",
       "HelpfulnessDenominator  568454.0  2.228810e+00  8.289740e+00          0.0   \n",
       "Score                   568454.0  4.183199e+00  1.310436e+00          1.0   \n",
       "Time                    568454.0  1.296257e+09  4.804331e+07  939340800.0   \n",
       "\n",
       "                                 25%           50%           75%           max  \n",
       "Id                      1.421142e+05  2.842275e+05  4.263408e+05  5.684540e+05  \n",
       "HelpfulnessNumerator    0.000000e+00  0.000000e+00  2.000000e+00  8.660000e+02  \n",
       "HelpfulnessDenominator  0.000000e+00  1.000000e+00  2.000000e+00  9.230000e+02  \n",
       "Score                   4.000000e+00  5.000000e+00  5.000000e+00  5.000000e+00  \n",
       "Time                    1.271290e+09  1.311120e+09  1.332720e+09  1.351210e+09  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c5990fa-f665-488b-8875-44ce00f419bb",
   "metadata": {},
   "source": [
    "basic statistics of our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df8b5cf7-17b4-40e8-ae4f-c301ef7b4a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than  most.'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Text\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c7cf0f-a49a-47e8-8ff2-6436ddca8f1d",
   "metadata": {},
   "source": [
    "an example of review. It has stopwords and punctuation marks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "869906d9-ec03-483e-94f6-e959a0c71467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I bought these for my husband who is currently overseas. He loves these, and apparently his staff likes them also.<br />There are generous amounts of Twizzlers in each 16-ounce bag, and this was well worth the price. <a href=\"http://www.amazon.com/gp/product/B001GVISJM\">Twizzlers, Strawberry, 16-Ounce Bags (Pack of 6)</a>'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Text\"][21]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66d79bde-7ca4-4b73-8f4e-56151dbba2ad",
   "metadata": {},
   "source": [
    "and it has numerical values that not necessary in sentiment analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7678c3c7-89fb-4478-9b10-52d4b5946c3c",
   "metadata": {},
   "source": [
    "## STEP2: Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f225eb37-42fc-4873-9ba8-a4cc7010d53e",
   "metadata": {},
   "source": [
    "- Have information about null variables\n",
    "- Question the outliers\n",
    "- Catch duplicated rows\n",
    "- Prepare data to text processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d12600c-078e-485a-b436-8d0e4cd8eff1",
   "metadata": {},
   "source": [
    "![STEPS](source/steps.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebbe5bd-df9c-4dd8-9269-52ed7e8dda3b",
   "metadata": {},
   "source": [
    "### Missing Observation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93dda7eb-c1cd-495c-8cb2-74fc78d833ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284bdf16-a09e-40eb-9fdf-3664c091794a",
   "metadata": {},
   "source": [
    "there is/are missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5e19b3f-b3b7-49e9-853d-7534f342a967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                         0\n",
       "ProductId                  0\n",
       "UserId                     0\n",
       "ProfileName               16\n",
       "HelpfulnessNumerator       0\n",
       "HelpfulnessDenominator     0\n",
       "Score                      0\n",
       "Time                       0\n",
       "Summary                   27\n",
       "Text                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4859d9fc-861b-4947-ab7e-4dfaadebbce6",
   "metadata": {},
   "source": [
    "we have empty values in ProfileName and summary (our process those are unnecessary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891adcb4-72ff-4302-a1be-5ac6c6459bec",
   "metadata": {},
   "source": [
    "totally 43 NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a4a39a-1b46-42c1-9adb-dddd8deb3c08",
   "metadata": {},
   "source": [
    "### Visualization Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b69cf5f6-7033-4a90-a971-744c35cc3ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca3ae97b-83ec-4129-a57f-bab3ac5873fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df, figsize=(10,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda9861a-37bb-451d-88c8-3acd8795f039",
   "metadata": {},
   "source": [
    "as you see in graph missing values are very few. It looks like there are no empty rows but we have"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956c0238-cdfc-45fa-b393-3fe3939b2b78",
   "metadata": {},
   "source": [
    "- IF I delete these empty values I lose every review text\n",
    "- so, I will not delete. I dont need them sentiment analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49cd0154-a533-47c3-9b57-8c2b50764aa1",
   "metadata": {},
   "source": [
    "### Outlier Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c6f718ee-f59f-48da-86e8-a842f26795f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df[\"Score\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77887998-e978-4400-8c90-65c14bcba6c3",
   "metadata": {},
   "source": [
    "Score labels outliers. They do not looks outliers.\n",
    "- scores are group between 4-5\n",
    "- so hard that find outliers in df\n",
    "- that's all about datasets structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e88c6bf-1b4c-490a-bb89-5b7f0abee7ba",
   "metadata": {},
   "source": [
    "### Duplicated Value Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "97f488c7-52c8-4e80-b0d1-9a17168ab815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f47f74-91f4-46ee-a318-99cc2a77b6e9",
   "metadata": {},
   "source": [
    "dulicated \"row\" count  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a8faba9-a6e4-4db9-8c56-cde3df3c858c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272711"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Summary\"].duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6617992-4e41-4317-924c-aa4f81d8ed4c",
   "metadata": {},
   "source": [
    "## STEP3: Text Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c6d557-59a9-4b1d-a8c8-6ef59e21228a",
   "metadata": {},
   "source": [
    "- Focus on texts for processing\n",
    "- Convert text to pandas dataframe (that's useful)\n",
    "- Catch HTML tags and get rid of (in our case we don't need them)\n",
    "- Remove punctuation marks\n",
    "- Remove numeric values because those are not emotional\n",
    "- Get lower case text to implement stopword process\n",
    "- Remove stopwords\n",
    "- Lemmatization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9e446c-5f88-4b7a-acab-046a4908c07b",
   "metadata": {},
   "source": [
    "![TEXT](source/text.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b79bba-98c1-4d7e-be15-4023a6bcc1cb",
   "metadata": {},
   "source": [
    "### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71e0d163-a136-44dc-9813-9b3638812637",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "from textblob import TextBlob\n",
    "from textblob import Word\n",
    "\n",
    "from nltk.stem import PorterStemmer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78707f25-965b-424d-b536-35d6907cf707",
   "metadata": {},
   "source": [
    "### Extract Text From Df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e0f2682-bf8d-408f-96c3-852d5b9e6ec3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "text = df[\"Text\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e8fa4cd6-2159-4e77-9408-dcebe03b63bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    I have bought several of the Vitality canned d...\n",
       "1    Product arrived labeled as Jumbo Salted Peanut...\n",
       "2    This is a confection that has been around a fe...\n",
       "3    If you are looking for the secret ingredient i...\n",
       "4    Great taffy at a great price.  There was a wid...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab975a0a-07d7-468d-97b7-ccddb9c45820",
   "metadata": {},
   "source": [
    "### Vectorize Sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8757b4c-0d05-4e09-aa8e-25e89d326ef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "r = pd.Series(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c4c3fa7d-8709-4553-8524-7ea939ee791f",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = pd.DataFrame(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "903bd5f0-0ed3-466d-a2ee-45be96437c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text\n",
       "0  I have bought several of the Vitality canned d...\n",
       "1  Product arrived labeled as Jumbo Salted Peanut...\n",
       "2  This is a confection that has been around a fe...\n",
       "3  If you are looking for the secret ingredient i...\n",
       "4  Great taffy at a great price.  There was a wid..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fc7476-8feb-4095-95ca-a91364cfcdef",
   "metadata": {},
   "source": [
    "### Delete HTML Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "13d1ae40-bbd8-4e6c-8471-3dc2e44aa124",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfa6829c-4190-4a6b-847a-fae01b6e6c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I bought these for my husband who is currently overseas. He loves these, and apparently his staff likes them also.<br />There are generous amounts of Twizzlers in each 16-ounce bag, and this was well worth the price. <a href=\"http://www.amazon.com/gp/product/B001GVISJM\">Twizzlers, Strawberry, 16-Ounce Bags (Pack of 6)</a>'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"][21]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52899784-f827-4585-95fa-e055f08a86d2",
   "metadata": {},
   "source": [
    "there are html tags in text. useless for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2d852658-cef1-4abe-a47e-e9b603794b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I bought these for my husband who is currently overseas. He loves these, and apparently his staff likes them also.There are generous amounts of Twizzlers in each 16-ounce bag, and this was well worth the price. Twizzlers, Strawberry, 16-Ounce Bags (Pack of 6)'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.sub('<.*?>', '', reviews[\"Text\"][21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ddad5550-9b59-49ba-aa40-3323f8343774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Thank you for having this product available to be purchased on Amazon.com. PLEASE DO NOT DISCONTINUE THIS PRODUCT FROM this website. If so, please let me know immediately so that I can purchase these candies. Please do not give out my personal contact or credit card information to other companies, especially without my personal permission or authorization to do so.<br />I am not sure about how to do this or whether I am even doing this correctly. Please explain this to me in a simple way for me to understand about this review process and how it works.<br /><br />Anna Mae Gardner'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"][220036]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cd71691e-8fba-46ca-8727-2a34ae33ece1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Thank you for having this product available to be purchased on Amazon.com. PLEASE DO NOT DISCONTINUE THIS PRODUCT FROM this website. If so, please let me know immediately so that I can purchase these candies. Please do not give out my personal contact or credit card information to other companies, especially without my personal permission or authorization to do so.I am not sure about how to do this or whether I am even doing this correctly. Please explain this to me in a simple way for me to understand about this review process and how it works.Anna Mae Gardner'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.sub('<.*?>','', reviews[\"Text\"][220036])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa77971a-19a4-4985-9452-4dbafe5a5e0c",
   "metadata": {},
   "source": [
    "#### Define Remove Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "58fff60e-514a-432d-8101-3b00f8aa571f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeTags(string):\n",
    "    r = re.sub('<.*?>','', string)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "74cec1ff-194c-48ae-a4e2-ef5cffa1fe00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Thank you for having this product available to be purchased on Amazon.com. PLEASE DO NOT DISCONTINUE THIS PRODUCT FROM this website. If so, please let me know immediately so that I can purchase these candies. Please do not give out my personal contact or credit card information to other companies, especially without my personal permission or authorization to do so.I am not sure about how to do this or whether I am even doing this correctly. Please explain this to me in a simple way for me to understand about this review process and how it works.Anna Mae Gardner'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "removeTags(reviews[\"Text\"][220036])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ccb74c4-6c64-4e3d-bac0-4f2f8aa237e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "withoutTags = reviews[\"Text\"].apply(lambda x: removeTags(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b26e94a-6d48-4bcc-96d5-4ff5b8ff602c",
   "metadata": {},
   "source": [
    "apply all text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2f1d89a8-fe51-43ea-b201-6ece3dc40c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = pd.DataFrame(withoutTags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c2df19b4-52e8-4832-a7ed-5badcf4f388d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I bought these for my husband who is currently overseas. He loves these, and apparently his staff likes them also.There are generous amounts of Twizzlers in each 16-ounce bag, and this was well worth the price. Twizzlers, Strawberry, 16-Ounce Bags (Pack of 6)'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"][21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "08575685-5358-41fd-9457-6b082d23d3d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Thank you for having this product available to be purchased on Amazon.com. PLEASE DO NOT DISCONTINUE THIS PRODUCT FROM this website. If so, please let me know immediately so that I can purchase these candies. Please do not give out my personal contact or credit card information to other companies, especially without my personal permission or authorization to do so.I am not sure about how to do this or whether I am even doing this correctly. Please explain this to me in a simple way for me to understand about this review process and how it works.Anna Mae Gardner'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"][220036]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e098f650-5021-4df4-8ce3-7ec8ddfc97f3",
   "metadata": {},
   "source": [
    "### Delete Numerical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4b825048-a1ee-40bc-93be-e4507ffe0a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "removeNumerics = reviews[\"Text\"].str.replace(\"\\d\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7217e614-3900-4649-8613-b7f1421f16e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "568449    Great for sesame chicken..this is a good if no...\n",
       "568450    I'm disappointed with the flavor. The chocolat...\n",
       "568451    These stars are small, so you can give - of th...\n",
       "568452    These are the BEST treats for training and rew...\n",
       "568453    I am very satisfied ,product is as advertised,...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "removeNumerics.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eef041f1-aeaf-4fa0-a8ad-a897fca46fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = pd.DataFrame(removeNumerics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "970fb3a7-a9fc-45c2-b69d-7bc9b8cad766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568449</th>\n",
       "      <td>Great for sesame chicken..this is a good if no...</td>\n",
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       "      <th>568450</th>\n",
       "      <td>I'm disappointed with the flavor. The chocolat...</td>\n",
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       "    <tr>\n",
       "      <th>568451</th>\n",
       "      <td>These stars are small, so you can give - of th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568452</th>\n",
       "      <td>These are the BEST treats for training and rew...</td>\n",
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       "      <th>568453</th>\n",
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       "                                                     Text\n",
       "568449  Great for sesame chicken..this is a good if no...\n",
       "568450  I'm disappointed with the flavor. The chocolat...\n",
       "568451  These stars are small, so you can give - of th...\n",
       "568452  These are the BEST treats for training and rew...\n",
       "568453  I am very satisfied ,product is as advertised,..."
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a1eb3d-4865-48ac-9935-7a6bb2619833",
   "metadata": {},
   "source": [
    "### Uppercase / Lowercase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f89a09b-3905-4442-8a99-0452c078f5f6",
   "metadata": {},
   "source": [
    "- Elimination of differences between texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b8f50559-6b75-4101-b810-e75f7e369445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         i have bought several of the vitality canned d...\n",
       "1         product arrived labeled as jumbo salted peanut...\n",
       "2         this is a confection that has been around a fe...\n",
       "3         if you are looking for the secret ingredient i...\n",
       "4         great taffy at a great price. there was a wide...\n",
       "                                ...                        \n",
       "568449    great for sesame chicken..this is a good if no...\n",
       "568450    i'm disappointed with the flavor. the chocolat...\n",
       "568451    these stars are small, so you can give - of th...\n",
       "568452    these are the best treats for training and rew...\n",
       "568453    i am very satisfied ,product is as advertised,...\n",
       "Name: Text, Length: 568454, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"].apply(lambda x: \" \".join(x.lower() for x in x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7fdadf40-dbfc-47cd-871a-2f08422638f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "toLower = reviews[\"Text\"].apply(lambda x: \" \".join(x.lower() for x in x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "51388dcc-05ec-4380-bd96-4144c6142bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>3</th>\n",
       "      <td>if you are looking for the secret ingredient i...</td>\n",
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       "      <th>4</th>\n",
       "      <td>great taffy at a great price. there was a wide...</td>\n",
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      "text/plain": [
       "                                                Text\n",
       "0  i have bought several of the vitality canned d...\n",
       "1  product arrived labeled as jumbo salted peanut...\n",
       "2  this is a confection that has been around a fe...\n",
       "3  if you are looking for the secret ingredient i...\n",
       "4  great taffy at a great price. there was a wide..."
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = pd.DataFrame(toLower)\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57fff7eb-34b2-477a-83ae-3438c2b3474a",
   "metadata": {},
   "source": [
    "### Stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37037748-7f7d-487a-ba32-5fd66e8caad0",
   "metadata": {},
   "source": [
    "- Stop words are basically a set of commonly used words in any language\n",
    "- Every language has stopwords\n",
    "\n",
    "\n",
    "- Determiners: Determiners tend to mark nouns where a determiner usually will be followed by a noun (the, a, an, another)\n",
    "- Coordinating conjunctions: Coordinating conjunctions connect words, phrases, and clauses (for, an, nor, but, or, yet, so)\n",
    "- Prepositions: Prepositions express temporal or spatial relations (in, under, towards, before)\n",
    "\n",
    "\n",
    "- All of them not represents emotions\n",
    "- We not interest none emotional words or marks\n",
    "\n",
    "The reason why stop words are critical to many applications is that, if we remove the words that are very commonly used in a given language, we can focus on the important words instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "db7f74bd-06f0-4fc4-b15f-e981aec65e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sw = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7d25ad16-9f39-4ca7-a9f0-e68239e3d079",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'me', 'my', 'myself', 'we']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "23915b28-18bc-4137-96dd-1b607dbebd1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         bought several vitality canned dog food produc...\n",
       "1         product arrived labeled jumbo salted peanuts.....\n",
       "2         confection around centuries. light, pillowy ci...\n",
       "3         looking secret ingredient robitussin believe f...\n",
       "4         great taffy great price. wide assortment yummy...\n",
       "                                ...                        \n",
       "568449    great sesame chicken..this good better restura...\n",
       "568450    i'm disappointed flavor. chocolate notes espec...\n",
       "568451    stars small, give - one training session. trie...\n",
       "568452    best treats training rewarding dog good groomi...\n",
       "568453    satisfied ,product advertised, use cereal, raw...\n",
       "Name: Text, Length: 568454, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"].apply(lambda x: \" \".join(x for x in x.split() if x not in sw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8b8b8531-3685-434a-86dd-bc0ed653797f",
   "metadata": {},
   "outputs": [],
   "source": [
    "withoutSW = reviews[\"Text\"].apply(lambda x: \" \".join(x for x in x.split() if x not in sw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8a03089a-be5a-44ac-8e1f-827ad20b6f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bought several vitality canned dog food produc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>product arrived labeled jumbo salted peanuts.....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>confection around centuries. light, pillowy ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>looking secret ingredient robitussin believe f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>great taffy great price. wide assortment yummy...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                                Text\n",
       "0  bought several vitality canned dog food produc...\n",
       "1  product arrived labeled jumbo salted peanuts.....\n",
       "2  confection around centuries. light, pillowy ci...\n",
       "3  looking secret ingredient robitussin believe f...\n",
       "4  great taffy great price. wide assortment yummy..."
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = pd.DataFrame(withoutSW)\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f14165b-b8f2-40c2-b9f3-6040f28e81b1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Delete Punctuation Marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a7f0de47-8931-4698-b30c-21aba85b8259",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>product arrived labeled jumbo salted peanuts.....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>confection around centuries. light, pillowy ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>looking secret ingredient robitussin believe f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>great taffy great price. wide assortment yummy...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text\n",
       "0  bought several vitality canned dog food produc...\n",
       "1  product arrived labeled jumbo salted peanuts.....\n",
       "2  confection around centuries. light, pillowy ci...\n",
       "3  looking secret ingredient robitussin believe f...\n",
       "4  great taffy great price. wide assortment yummy..."
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8ce876b2-2b22-4711-9be5-0954ea1fdd59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568449</th>\n",
       "      <td>great sesame chicken..this good better restura...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568450</th>\n",
       "      <td>i'm disappointed flavor. chocolate notes espec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568451</th>\n",
       "      <td>stars small, give - one training session. trie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568452</th>\n",
       "      <td>best treats training rewarding dog good groomi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568453</th>\n",
       "      <td>satisfied ,product advertised, use cereal, raw...</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                                     Text\n",
       "568449  great sesame chicken..this good better restura...\n",
       "568450  i'm disappointed flavor. chocolate notes espec...\n",
       "568451  stars small, give - one training session. trie...\n",
       "568452  best treats training rewarding dog good groomi...\n",
       "568453  satisfied ,product advertised, use cereal, raw..."
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f45d4780-7922-4308-a41f-681f6c8e7b0c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "removeMarks = reviews[\"Text\"].str.replace(\"[^\\w\\s]\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "293eb29a-7fbd-43fe-8a69-065d1db0c027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "568449    great sesame chickenthis good better resturant...\n",
       "568450    im disappointed flavor chocolate notes especia...\n",
       "568451    stars small give  one training session tried t...\n",
       "568452    best treats training rewarding dog good groomi...\n",
       "568453    satisfied product advertised use cereal raw vi...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "removeMarks.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "be2bf366-49bb-4416-a01c-0943498ab6a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = pd.DataFrame(removeMarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f84d8465-bbf6-4c27-9f37-ffe2a0a82986",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>stars small give  one training session tried t...</td>\n",
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       "      <td>best treats training rewarding dog good groomi...</td>\n",
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       "      <th>568453</th>\n",
       "      <td>satisfied product advertised use cereal raw vi...</td>\n",
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      "text/plain": [
       "                                                     Text\n",
       "568449  great sesame chickenthis good better resturant...\n",
       "568450  im disappointed flavor chocolate notes especia...\n",
       "568451  stars small give  one training session tried t...\n",
       "568452  best treats training rewarding dog good groomi...\n",
       "568453  satisfied product advertised use cereal raw vi..."
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a60b90a-7f52-401e-a995-69abba1c00a4",
   "metadata": {},
   "source": [
    "### Remove Infrecuent Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "64df36d5-38e1-43e1-840b-73d8558eacbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "infrequent = pd.Series(\" \".join(reviews[\"Text\"]).split()).value_counts()[-200000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7e7d2210-cbc8-4698-a720-1340aa5689aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "icesnow           2\n",
       "speakmmrammmrr    2\n",
       "availableboth     2\n",
       "twolegs           2\n",
       "barbequeing       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infrequent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "201644db-ddfa-4682-9067-172ab350c85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barehanded      1\n",
       "ocasionsi       1\n",
       "ctce            1\n",
       "appetizeradd    1\n",
       "nonlocal        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infrequent.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c6a3e321-723d-474c-b823-e285677b35e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000,)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infrequent.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af9624cc-f761-4e29-99d0-6345ae7583fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "frequent = reviews[\"Text\"].apply(lambda x: \" \".join(x for x in x.split() if x not in infrequent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b5150bde-3e94-48c3-b723-0b1b38854d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    bought several vitality canned dog food produc...\n",
       "1    product arrived labeled jumbo salted peanutsth...\n",
       "2    confection around centuries light pillowy citr...\n",
       "3    looking secret ingredient robitussin believe f...\n",
       "4    great taffy great price wide assortment yummy ...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "48619662-b887-470a-972f-dad8a213a342",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = pd.DataFrame(frequent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "52b6ba9e-5be1-4bb4-a61c-adb42da496b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
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       "      <th>3</th>\n",
       "      <td>looking secret ingredient robitussin believe f...</td>\n",
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       "      <th>4</th>\n",
       "      <td>great taffy great price wide assortment yummy ...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text\n",
       "0  bought several vitality canned dog food produc...\n",
       "1  product arrived labeled jumbo salted peanutsth...\n",
       "2  confection around centuries light pillowy citr...\n",
       "3  looking secret ingredient robitussin believe f...\n",
       "4  great taffy great price wide assortment yummy ..."
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af88551c-3dcb-4ed2-a8a5-9b4dfe1971b5",
   "metadata": {},
   "source": [
    "### Lemmatization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f49fb6-72ea-4b7a-af54-b28b77057a02",
   "metadata": {},
   "source": [
    "- The purpose of lemmatization is same as that of stemming but overcomes the drawbacks of stemming. In stemming, for some words, it may not give may not give meaningful representation such as “Histori”. Here, lemmatization comes into picture as it gives meaningful word\n",
    "\n",
    "- Lemmatization takes more time as compared to stemming because it finds meaningful word/ representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ba7c28f1-4c55-4d37-b96e-8737915acd5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatized  = reviews[\"Text\"].apply(lambda x: \" \".join([Word(i).lemmatize() for i in x.split()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "af0bd213-d147-4049-b8d3-8e9c929e1cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    bought several vitality canned dog food produc...\n",
       "1    product arrived labeled jumbo salted peanutsth...\n",
       "2    confection around century light pillowy citrus...\n",
       "3    looking secret ingredient robitussin believe f...\n",
       "4    great taffy great price wide assortment yummy ...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemmatized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dc20cbc2-03e8-4fb9-8c22-7924b7012b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "568449    great sesame chickenthis good better resturant...\n",
       "568450    im disappointed flavor chocolate note especial...\n",
       "568451    star small give one training session tried tra...\n",
       "568452    best treat training rewarding dog good groomin...\n",
       "568453    satisfied product advertised use cereal raw vi...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemmatized.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "335b0332-d891-4c2d-b479-1d68b0363afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'thank product available purchased amazoncom please discontinue product website so please let know immediately purchase candy please give personal contact credit card information company especially without personal permission authorization soi sure whether even correctly please explain simple way understand review process mae gardner'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemmatized[220036]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fbe1218e-be30-4a1a-8dc3-cc6721d25bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'thank product available purchased amazoncom please discontinue product website so please let know immediately purchase candies please give personal contact credit card information companies especially without personal permission authorization soi sure whether even correctly please explain simple way understand review process mae gardner'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"][220036]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d9a3c8a4-3903-4566-b124-f2a91e1fca2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = pd.DataFrame(lemmatized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "46b0a6f6-5de3-43d8-9138-41e77c93f3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568449</th>\n",
       "      <td>great sesame chickenthis good better resturant...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568450</th>\n",
       "      <td>im disappointed flavor chocolate note especial...</td>\n",
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       "    <tr>\n",
       "      <th>568451</th>\n",
       "      <td>star small give one training session tried tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568452</th>\n",
       "      <td>best treat training rewarding dog good groomin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568453</th>\n",
       "      <td>satisfied product advertised use cereal raw vi...</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                                                     Text\n",
       "568449  great sesame chickenthis good better resturant...\n",
       "568450  im disappointed flavor chocolate note especial...\n",
       "568451  star small give one training session tried tra...\n",
       "568452  best treat training rewarding dog good groomin...\n",
       "568453  satisfied product advertised use cereal raw vi..."
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d900385f-9df5-434f-862c-5ca6603728d1",
   "metadata": {},
   "source": [
    "there are 100.000 infrequent words here. this amount change what value I want"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da2a2be-0e5d-4a06-9933-de1ce5cf0d9f",
   "metadata": {},
   "source": [
    "## STEP4: Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d85130-df17-4f17-9364-1f6751e4374a",
   "metadata": {},
   "source": [
    "### Part Of Speech Tagging"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7275c970-234d-4e7d-90c7-c429fc4ab958",
   "metadata": {},
   "source": [
    "![POS](source/pos.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affa816c-43df-4db5-8891-c1df034337de",
   "metadata": {},
   "source": [
    "- POS is substructures of a sentence\n",
    "- prepositions, verbs, nouns, adjectives etc.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f3b06872-8908-43d6-88e5-486d8d2182f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "929030d4-56aa-42ca-9a33-6e08a16ba585",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bought', 'VBD'),\n",
       " ('several', 'JJ'),\n",
       " ('vitality', 'NN'),\n",
       " ('canned', 'VBD'),\n",
       " ('dog', 'JJ'),\n",
       " ('food', 'NN'),\n",
       " ('product', 'NN'),\n",
       " ('found', 'VBD'),\n",
       " ('good', 'JJ'),\n",
       " ('quality', 'NN'),\n",
       " ('product', 'NN'),\n",
       " ('look', 'NN'),\n",
       " ('like', 'IN'),\n",
       " ('stew', 'NNS'),\n",
       " ('processed', 'VBN'),\n",
       " ('meat', 'NN'),\n",
       " ('smell', 'NN'),\n",
       " ('better', 'RBR'),\n",
       " ('labrador', 'NN'),\n",
       " ('finicky', 'JJ'),\n",
       " ('appreciates', 'VBZ'),\n",
       " ('product', 'NN'),\n",
       " ('better', 'RBR'),\n",
       " ('most', 'RBS')]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob(reviews[\"Text\"][0]).tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fa000442-6a23-4395-a9ec-33ebb7ba1b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('I', 'PRP'), ('am', 'VBP'), ('so', 'RB'), ('happy', 'JJ')]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob(\"I am so happy\").tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "71f509d7-9892-4d31-aacf-5ade585f8d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('so', 'RB'), ('I', 'PRP'), ('am', 'VBP'), ('happy', 'JJ')]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob(\"so I am happy\").tags"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fdd047e-06d2-424e-be44-63ed580304d6",
   "metadata": {},
   "source": [
    "### Named Entity Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "de662326-6e23-4ded-bb9a-020956c0c43f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import ne_chunk, word_tokenize, pos_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2e54ee07-4999-4fc2-928a-ddf3201565d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk = ne_chunk(pos_tag(word_tokenize(reviews[\"Text\"][0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2d88e879-30da-4a02-897d-73eb2e59b7b5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  bought/VBD\n",
      "  several/JJ\n",
      "  vitality/NN\n",
      "  canned/VBD\n",
      "  dog/JJ\n",
      "  food/NN\n",
      "  product/NN\n",
      "  found/VBD\n",
      "  good/JJ\n",
      "  quality/NN\n",
      "  product/NN\n",
      "  look/NN\n",
      "  like/IN\n",
      "  stew/NNS\n",
      "  processed/VBN\n",
      "  meat/NN\n",
      "  smell/NN\n",
      "  better/RBR\n",
      "  labrador/NN\n",
      "  finicky/JJ\n",
      "  appreciates/VBZ\n",
      "  product/NN\n",
      "  better/RBR\n",
      "  most/RBS)\n"
     ]
    }
   ],
   "source": [
    "print(ne_chunk(pos_tag(word_tokenize(reviews[\"Text\"][0]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "226bb2ca-1451-46b6-82c8-6ee315d07849",
   "metadata": {},
   "outputs": [],
   "source": [
    "regEx = \"NP: {<DT>?<JJ>*<NN>}\"\n",
    "rp = nltk.RegexpParser(regEx)\n",
    "res = rp.parse(chunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "aa109d8e-58b2-40c3-815d-b5173f835cea",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  bought/VBD\n",
      "  (NP several/JJ vitality/NN)\n",
      "  canned/VBD\n",
      "  (NP dog/JJ food/NN)\n",
      "  (NP product/NN)\n",
      "  found/VBD\n",
      "  (NP good/JJ quality/NN)\n",
      "  (NP product/NN)\n",
      "  (NP look/NN)\n",
      "  like/IN\n",
      "  stew/NNS\n",
      "  processed/VBN\n",
      "  (NP meat/NN)\n",
      "  (NP smell/NN)\n",
      "  better/RBR\n",
      "  (NP labrador/NN)\n",
      "  finicky/JJ\n",
      "  appreciates/VBZ\n",
      "  (NP product/NN)\n",
      "  better/RBR\n",
      "  most/RBS)\n"
     ]
    }
   ],
   "source": [
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e7f651-f1b7-47ca-ba90-982004bff766",
   "metadata": {},
   "source": [
    "### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "810f2f07-3e4a-44a3-ac26-3a28bb21718f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         166\n",
       "1         135\n",
       "2         282\n",
       "3         137\n",
       "4          83\n",
       "         ... \n",
       "568449     93\n",
       "568450    164\n",
       "568451    238\n",
       "568452    115\n",
       "568453     68\n",
       "Name: Text, Length: 568454, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find each review has how many letter\n",
    "reviews[\"Text\"].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f91ec5e8-dccf-48d9-be6a-1725ffab2c3c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         24\n",
       "1         18\n",
       "2         41\n",
       "3         20\n",
       "4         13\n",
       "          ..\n",
       "568449    14\n",
       "568450    26\n",
       "568451    38\n",
       "568452    18\n",
       "568453     9\n",
       "Name: Text, Length: 568454, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find each review has how many word\n",
    "reviews[\"Text\"].apply(lambda x: len(str(x).split(\" \")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1525285d-226e-4964-860b-e25672f756dd",
   "metadata": {},
   "source": [
    "### Vader Sentiment Scoring"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aefe2e5-4d0c-4f9c-b6d9-f55302d53e39",
   "metadata": {},
   "source": [
    "- Vader : Valence Aware Dictionary and Sentiment Reasoner\n",
    "\n",
    "It is used for sentiment analysis of text which has both the polarities i.e. positive/negative. VADER is used to quantify how much of positive or negative emotion the text has and also the intensity of emotion.\n",
    "\n",
    "- It does not require any training data.\n",
    "- It can very well understand the sentiment of a text containing emoticons, slangs, conjunctions, capital words, punctuations and much more.\n",
    "- It works excellent on social media text.\n",
    "- VADER can work with multiple domains."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3dd0df-5538-4065-9906-e8129909a196",
   "metadata": {},
   "source": [
    "![POS](source/vader.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9090a062-c135-4524-b37b-1c3eed31ea3c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9157c9cf-1026-4bf3-a52c-5fb086f4c2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nltk.sentiment.vader.SentimentIntensityAnalyzer at 0x260b3e46dc0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia = SentimentIntensityAnalyzer()\n",
    "sia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4d258517-405f-4a38-989b-a2d16382be46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.0, 'neu': 0.334, 'pos': 0.666, 'compound': 0.6115}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia.polarity_scores(\"I am so happy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "95e34da0-8ee0-46b5-9f24-ac2d10a3a27b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.52, 'neu': 0.48, 'pos': 0.0, 'compound': -0.7579}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia.polarity_scores(\"I hate human who talks to much in vain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "253e4452-0463-4cad-a2dd-5c553f0280b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bought several vitality canned dog food product found good quality product look like stew processed meat smell better labrador finicky appreciates product better most'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Text\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "71d85acd-e5d0-4eb7-bc58-af97b3fe6936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.0, 'neu': 0.517, 'pos': 0.483, 'compound': 0.9413}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia.polarity_scores(reviews[\"Text\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "47c68eef-447e-46a8-8107-c965abec7ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.129, 'neu': 0.762, 'pos': 0.11, 'compound': -0.1027}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia.polarity_scores(reviews[\"Text\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c41c2b4-e09d-437e-854f-bbcf349b3e8f",
   "metadata": {},
   "source": [
    "#### Apply Scoring Whole Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "46a16f93-f8c3-4c29-9339-dd76660f4c34",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "reviews = reviews.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "57da1a2b-8c5f-451c-8ef0-2ae22434a262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>bought several vitality canned dog food produc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>product arrived labeled jumbo salted peanutsth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>confection around century light pillowy citrus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>looking secret ingredient robitussin believe f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>great taffy great price wide assortment yummy ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                               Text\n",
       "0      0  bought several vitality canned dog food produc...\n",
       "1      1  product arrived labeled jumbo salted peanutsth...\n",
       "2      2  confection around century light pillowy citrus...\n",
       "3      3  looking secret ingredient robitussin believe f...\n",
       "4      4  great taffy great price wide assortment yummy ..."
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "70a280b6-d1b5-451c-ab5a-74d06bfd6113",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vader = reviews[\"Text\"].apply(lambda x: sia.polarity_scores(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "73ff19e3-0fa7-43b1-9c9b-72a040bd823c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    {'neg': 0.0, 'neu': 0.517, 'pos': 0.483, 'comp...\n",
       "1    {'neg': 0.129, 'neu': 0.762, 'pos': 0.11, 'com...\n",
       "2    {'neg': 0.13, 'neu': 0.584, 'pos': 0.286, 'com...\n",
       "3    {'neg': 0.0, 'neu': 0.868, 'pos': 0.132, 'comp...\n",
       "4    {'neg': 0.0, 'neu': 0.369, 'pos': 0.631, 'comp...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1d042852-22c5-4a16-b41f-7d3a93841045",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaderDf = pd.DataFrame()\n",
    "vaderDf[\"neg\"] = vader.apply(lambda x: x.get(\"neg\"))\n",
    "vaderDf[\"neu\"] = vader.apply(lambda x: x.get(\"neu\"))\n",
    "vaderDf[\"pos\"] = vader.apply(lambda x: x.get(\"pos\"))\n",
    "vaderDf[\"compound\"] = vader.apply(lambda x: x.get(\"compound\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1c292406-21ec-43e2-acbd-41bf4fac5182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.483</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.129</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.1027</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.130</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.8624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.4404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.9468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     neg    neu    pos  compound\n",
       "0  0.000  0.517  0.483    0.9413\n",
       "1  0.129  0.762  0.110   -0.1027\n",
       "2  0.130  0.584  0.286    0.8624\n",
       "3  0.000  0.868  0.132    0.4404\n",
       "4  0.000  0.369  0.631    0.9468"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaderDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "35690cea-3644-4015-aabf-8cbe27eeb6d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
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       "      <td>0.483</td>\n",
       "      <td>0.9413</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.8624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.4404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.9468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568449</th>\n",
       "      <td>568449</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568450</th>\n",
       "      <td>568450</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.7841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568451</th>\n",
       "      <td>568451</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.8860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568452</th>\n",
       "      <td>568452</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.9738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568453</th>\n",
       "      <td>568453</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.4215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568454 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index    neg    neu    pos  compound\n",
       "0            0  0.000  0.517  0.483    0.9413\n",
       "1            1  0.129  0.762  0.110   -0.1027\n",
       "2            2  0.130  0.584  0.286    0.8624\n",
       "3            3  0.000  0.868  0.132    0.4404\n",
       "4            4  0.000  0.369  0.631    0.9468\n",
       "...        ...    ...    ...    ...       ...\n",
       "568449  568449  0.000  0.420  0.580    0.9300\n",
       "568450  568450  0.320  0.548  0.132   -0.7841\n",
       "568451  568451  0.000  0.749  0.251    0.8860\n",
       "568452  568452  0.059  0.243  0.697    0.9738\n",
       "568453  568453  0.000  0.741  0.259    0.4215\n",
       "\n",
       "[568454 rows x 5 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaderDf.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860f05c1-3093-471d-a459-753399c7dc81",
   "metadata": {},
   "source": [
    "#### Concat Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4ac6570d-d606-4bf1-ade5-ec70d5d27c96",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vaderDf = pd.concat([reviews, vaderDf], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "96afa203-c79c-487d-b254-a1250155eef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Text</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>bought several vitality canned dog food produc...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.9413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>product arrived labeled jumbo salted peanutsth...</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>confection around century light pillowy citrus...</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.8624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>looking secret ingredient robitussin believe f...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.4404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>great taffy great price wide assortment yummy ...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.9468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                               Text    neg    neu  \\\n",
       "0      0  bought several vitality canned dog food produc...  0.000  0.517   \n",
       "1      1  product arrived labeled jumbo salted peanutsth...  0.129  0.762   \n",
       "2      2  confection around century light pillowy citrus...  0.130  0.584   \n",
       "3      3  looking secret ingredient robitussin believe f...  0.000  0.868   \n",
       "4      4  great taffy great price wide assortment yummy ...  0.000  0.369   \n",
       "\n",
       "     pos  compound  \n",
       "0  0.483    0.9413  \n",
       "1  0.110   -0.1027  \n",
       "2  0.286    0.8624  \n",
       "3  0.132    0.4404  \n",
       "4  0.631    0.9468  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaderDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cc7b0f62-e0d1-41c3-b90c-583953e56571",
   "metadata": {},
   "outputs": [
    {
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       "      <th>568452</th>\n",
       "      <td>568452</td>\n",
       "      <td>best treat training rewarding dog good groomin...</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.9738</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.4215</td>\n",
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      "text/plain": [
       "         index                                               Text    neg  \\\n",
       "568449  568449  great sesame chickenthis good better resturant...  0.000   \n",
       "568450  568450  im disappointed flavor chocolate note especial...  0.320   \n",
       "568451  568451  star small give one training session tried tra...  0.000   \n",
       "568452  568452  best treat training rewarding dog good groomin...  0.059   \n",
       "568453  568453  satisfied product advertised use cereal raw vi...  0.000   \n",
       "\n",
       "          neu    pos  compound  \n",
       "568449  0.420  0.580    0.9300  \n",
       "568450  0.548  0.132   -0.7841  \n",
       "568451  0.749  0.251    0.8860  \n",
       "568452  0.243  0.697    0.9738  \n",
       "568453  0.741  0.259    0.4215  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
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   ],
   "source": [
    "vaderDf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c87c2475-2d72-4a12-870b-f22861c5137c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568454, 6)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaderDf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5f25e452-de9e-44ae-b779-dea5aa348847",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaders = pd.DataFrame()\n",
    "vaders = pd.concat([vaderDf, df.Score], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "97941dd8-2441-49be-acd2-f56038827b18",
   "metadata": {},
   "outputs": [
    {
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       "      <td>product arrived labeled jumbo salted peanutsth...</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.110</td>\n",
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       "      <td>0.130</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.8624</td>\n",
       "      <td>4</td>\n",
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       "      <th>3</th>\n",
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       "      <td>looking secret ingredient robitussin believe f...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.4404</td>\n",
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       "      <td>0.369</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.9468</td>\n",
       "      <td>5</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                               Text    neg    neu  \\\n",
       "0      0  bought several vitality canned dog food produc...  0.000  0.517   \n",
       "1      1  product arrived labeled jumbo salted peanutsth...  0.129  0.762   \n",
       "2      2  confection around century light pillowy citrus...  0.130  0.584   \n",
       "3      3  looking secret ingredient robitussin believe f...  0.000  0.868   \n",
       "4      4  great taffy great price wide assortment yummy ...  0.000  0.369   \n",
       "\n",
       "     pos  compound  Score  \n",
       "0  0.483    0.9413      5  \n",
       "1  0.110   -0.1027      1  \n",
       "2  0.286    0.8624      4  \n",
       "3  0.132    0.4404      2  \n",
       "4  0.631    0.9468      5  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaders.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76a1a67-c12b-4743-8b90-aa30dade69d7",
   "metadata": {},
   "source": [
    "#### Plot VADER Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "77e67fba-93ab-48a7-a0b1-2df30db32c17",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(data=vaders, x = 'Score', y='compound')\n",
    "ax.set_title(\"Compound Scores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "6ca26902-ffb4-4e64-8f73-248e61ef0a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=vaders, x='Score', y='pos');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5d128185-515a-456d-b6d9-25b57bcf622a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,3,figsize=(15, 5))\n",
    "sns.barplot(data=vaders, x=\"Score\", y=\"pos\", ax=axs[0])\n",
    "sns.barplot(data=vaders, x=\"Score\", y=\"neu\", ax=axs[1])\n",
    "sns.barplot(data=vaders, x=\"Score\", y=\"neg\", ax=axs[2])\n",
    "axs[0].set_title(\"Positive\")\n",
    "axs[1].set_title(\"Neutral\")\n",
    "axs[2].set_title(\"negative\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760902ea-2d3b-46ae-917c-da5731c307f4",
   "metadata": {},
   "source": [
    "Neutral sentiment has very similar product scoring points as we wait."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "03814ad7-538a-4c81-8398-674029290927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 582.375x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=vaders, vars=[\"neg\", \"neu\", \"pos\"], hue=\"Score\", palette=\"tab10\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12a6605-9722-45e4-af45-f91478d8a616",
   "metadata": {},
   "source": [
    "This graphic shows us how scoring distributions on sentiment. As you see we have 5 scored negative reviews or 1 scored positive sentiments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821a9e58-1fec-47fa-8709-7ece42cf952b",
   "metadata": {},
   "source": [
    "### Visualization With WordCloud"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d80312-5d73-4366-8344-c7bf4a56fdc9",
   "metadata": {},
   "source": [
    "Visualize more frequent words with wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "7e70a604-38eb-4178-b405-410bfe5c46d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f8b3c6cd-0cd3-4acc-a8f3-7f95fd29cdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc = WordCloud(min_font_size=4, max_words=100).generate(frequent[0] + frequent[1] + frequent[2] + frequent[3] + frequent[4] + frequent[5])\n",
    "plt.figure(figsize=(16,8)) \n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "170265fc-422c-42d7-adc2-9c716b4f6faa",
   "metadata": {},
   "source": [
    "### Feature Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc76158-80a3-4041-9814-0b29488d15a0",
   "metadata": {},
   "source": [
    "Machines cannot understand characters and words. So when dealing with text data we need to represent it in numbers to be understood by the machine."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea01de7-a0ad-47e0-a2aa-c096343d42c5",
   "metadata": {},
   "source": [
    "#### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "251135c8-627c-497a-8545-4cdcd904b3df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import decomposition, ensemble, preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e746813-6020-470e-ac48-4ff75b8c1d49",
   "metadata": {},
   "source": [
    "### Encoding: Numeric Scoring & Categorization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6bee81-cc72-4b5d-a039-ec5b301c53e5",
   "metadata": {},
   "source": [
    "The performance of a machine learning model not only depends on the model and the hyperparameters but also on how we process and feed different types of variables to the model. \n",
    "Since most machine learning models only accept numerical variables, preprocessing the categorical variables becomes a necessary step.\n",
    "We need to convert these categorical variables to numbers such that the model is able to understand and extract valuable information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891f54ef-63de-4e68-9b43-d86403ab02c0",
   "metadata": {},
   "source": [
    "SScoring encoded, it means, it numerized instead of sentiment categorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "df6cfb44-4f3d-4fed-a613-17e90c8e53eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaders[\"SScoring\"] = vaders[\"compound\"].apply(lambda x: 0 if x< 0.25 else 1 if (x>= 0.25 and x<= 0.6) else 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3b74f313-2bff-43d7-baa3-256ea3e0d619",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaders[\"Sentiment\"] = vaders[\"compound\"].apply(lambda x: \"neg\" if x< 0.25 else \"neu\" if (x>= 0.25 and x<= 0.6) else \"pos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "08c41643-410e-4b6d-95af-d952182569c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Text</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>Score</th>\n",
       "      <th>SScoring</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>bought several vitality canned dog food produc...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.9413</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>product arrived labeled jumbo salted peanutsth...</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>confection around century light pillowy citrus...</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.8624</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>looking secret ingredient robitussin believe f...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>great taffy great price wide assortment yummy ...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.9468</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                               Text    neg    neu  \\\n",
       "0      0  bought several vitality canned dog food produc...  0.000  0.517   \n",
       "1      1  product arrived labeled jumbo salted peanutsth...  0.129  0.762   \n",
       "2      2  confection around century light pillowy citrus...  0.130  0.584   \n",
       "3      3  looking secret ingredient robitussin believe f...  0.000  0.868   \n",
       "4      4  great taffy great price wide assortment yummy ...  0.000  0.369   \n",
       "\n",
       "     pos  compound  Score  SScoring Sentiment  \n",
       "0  0.483    0.9413      5         2       pos  \n",
       "1  0.110   -0.1027      1         0       neg  \n",
       "2  0.286    0.8624      4         2       pos  \n",
       "3  0.132    0.4404      2         1       neu  \n",
       "4  0.631    0.9468      5         2       pos  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1984dac2-491a-42df-a8b4-d8f5902aad2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Text</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>Score</th>\n",
       "      <th>SScoring</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568449</th>\n",
       "      <td>568449</td>\n",
       "      <td>great sesame chickenthis good better resturant...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.9300</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568450</th>\n",
       "      <td>568450</td>\n",
       "      <td>im disappointed flavor chocolate note especial...</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.7841</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568451</th>\n",
       "      <td>568451</td>\n",
       "      <td>star small give one training session tried tra...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.8860</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568452</th>\n",
       "      <td>568452</td>\n",
       "      <td>best treat training rewarding dog good groomin...</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.9738</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568453</th>\n",
       "      <td>568453</td>\n",
       "      <td>satisfied product advertised use cereal raw vi...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.4215</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index                                               Text    neg  \\\n",
       "568449  568449  great sesame chickenthis good better resturant...  0.000   \n",
       "568450  568450  im disappointed flavor chocolate note especial...  0.320   \n",
       "568451  568451  star small give one training session tried tra...  0.000   \n",
       "568452  568452  best treat training rewarding dog good groomin...  0.059   \n",
       "568453  568453  satisfied product advertised use cereal raw vi...  0.000   \n",
       "\n",
       "          neu    pos  compound  Score  SScoring Sentiment  \n",
       "568449  0.420  0.580    0.9300      5         2       pos  \n",
       "568450  0.548  0.132   -0.7841      2         0       neg  \n",
       "568451  0.749  0.251    0.8860      5         2       pos  \n",
       "568452  0.243  0.697    0.9738      5         2       pos  \n",
       "568453  0.741  0.259    0.4215      5         1       neu  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaders.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75c3df2-c5de-4219-829e-eea71bbd2f36",
   "metadata": {},
   "source": [
    "### Export DF and Avoid Rerunning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18aa67b-1745-4d19-9a3d-2feacc32bfde",
   "metadata": {},
   "source": [
    "Export VADER results for avoid rerunning whole notebook. It is created ones and use easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5f70123e-b0d0-47c2-b8f2-02f9b84d8ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vaders.to_csv(r'vaders.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e92318d7-0b83-4f35-ba51-41856b0abdb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaders = pd.read_csv('vaders.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6154d83a-72d1-4222-a79f-fb4c27a8cd2d",
   "metadata": {},
   "source": [
    "import csv file "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88930c76-e972-4faa-8d7d-6fa32e6fd916",
   "metadata": {},
   "source": [
    "### Train & Test Decomposition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837f4906-95fa-4ed6-9d39-25717d9302d3",
   "metadata": {},
   "source": [
    "On supervised learning we need to train algorithms with datasets train part and testing that trained dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6884e6a9-40f9-4b99-988b-53fa180953d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(vaders[\"Text\"], vaders[\"SScoring\"], test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6b20dc2-8ecb-409e-bc3f-62a5ae4b71c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "376903    dog usually eat anything vomited hour period f...\n",
       "445243    excited find item since lipton changed recipe ...\n",
       "226194    salt hickory smoked sea salt durango great bbq...\n",
       "456411    ideal rack kcups especially good limited count...\n",
       "327413    son month old starting spitting regular enfami...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d27b4584-fb28-4c3d-892f-1ee85ed93ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "376903    0\n",
       "445243    2\n",
       "226194    2\n",
       "456411    2\n",
       "327413    1\n",
       "Name: SScoring, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ede3dce-a555-42a0-b576-9edb155bb93e",
   "metadata": {},
   "source": [
    "### Count Vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97243c2b-e28f-459e-9cee-641f24855fde",
   "metadata": {},
   "source": [
    " It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, \n",
    " and we wish to convert each word in each text into vectors (for using in further text analysis).\n",
    "\n",
    "- Countvectorizer is a method to convert text to numerical data.\n",
    "- The text transforms to a sparse matrix.\n",
    "- Machine learning algorithms cannot work with raw text directly. Rather, the text must be converted into vectors of numbers. In natural language processing, \n",
    "a common technique for extracting features from text is to place all of the words that occur in the text in a bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e24c378d-ab9a-40f8-905e-ac0cde1b97c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(x_train.values.astype('U'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd50442a-f179-4322-8214-5f7b11d7b8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_count = vectorizer.transform(x_train.values.astype('U'))\n",
    "x_test_count = vectorizer.transform(x_test.values.astype('U'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a59fb5b3-af7c-41e7-9f0f-d20113bc295f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_can_', '_exactly_', '_finally_', '_i', '_i_']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_feature_names()[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fdc85aa6-eeb4-403b-b82b-3f164bef357c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(<426340x100315 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 14410624 stored elements in Compressed Sparse Row format>,\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(x_train_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c92893a-47ce-4535-b920-d4a03ad520bc",
   "metadata": {},
   "source": [
    "### TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3984e033-df4d-4725-8b03-f76f3fcc7de7",
   "metadata": {},
   "source": [
    "Tf-Idf are numerical statistics: Term frequency(tf) and inverse document frequency(idf) are shows that a word how important on the document.\n",
    "\n",
    "\n",
    "- Tf(t) = The number of times a word appears in a document divded by the total number of words in the document. Every document has its own term frequency\n",
    "- IDF(t) = The log of the number of documents divided by the number of documents that contain the word w. Inverse data frequency determines the weight of rare words across all documents in the corpus\n",
    "- It can be defined as the calculation of how relevant a word in a series or corpus is to a text.\n",
    "- OR How to find meaning of sentences and documents.\n",
    "\n",
    "\n",
    "- I found another explaining => Tf-Idf is a technique which is used to find meaning of sentences consisting of words and cancels out the incapabilities of Bag of Words technique which is good for text classification or for helping a machine read words in numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724a54e7-a540-4d13-bbe2-3e00acb058a5",
   "metadata": {},
   "source": [
    "![TFIDF](source/tfidf.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95fd433a-c393-414c-b083-aaae453e41d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_word_vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0623ce2f-7fae-4459-8647-36c0cd3719dc",
   "metadata": {},
   "source": [
    "#### custom n-gram level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf13d106-756f-4e1b-b8a7-4d032eab7388",
   "metadata": {},
   "source": [
    "Wikipedia defines an N-Gram as “A contiguous sequence of N items from a given sample of text or speech”.\n",
    "\n",
    "- Here an item can be a character(char level), a word or a sentence and N can be any integer. When N is 2, we call the sequence a bigram. Similarly, a sequence of 3 items is called a trigram, and so on.\n",
    "- The N-Gram model is one of the best sentence-to-vector models, since it utilizes the context between N-words in a sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10edce02-1bcd-48cd-9eee-6795ea7d1265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(ngram_range=(1, 2))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(ngram_range=(1, 2))</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer(ngram_range=(1, 2))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_ngram_vectorizer = TfidfVectorizer(ngram_range = (1,2))\n",
    "tf_idf_ngram_vectorizer.fit(x_train.apply(lambda x: np.str_(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5dc0c776-efaa-48b8-9c88-2e0d246506f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['___ instead water_',\n",
       " '___ this',\n",
       " '___ this bit',\n",
       " '___ variety',\n",
       " '___ variety calculated']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_ngram_vectorizer.get_feature_names()[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2241145e-624f-4556-be58-2bc0b66aa5d3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train_tf_idf_ngram = tf_idf_ngram_vectorizer.transform(x_train.apply(lambda x: np.str_(x)))\n",
    "x_test_tf_idf_ngram = tf_idf_ngram_vectorizer.transform(x_test.apply(lambda x: np.str_(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba0a371-c709-413e-aad1-70374d07baee",
   "metadata": {},
   "source": [
    "#### word level n_gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9f4ce82-0dd4-4330-b704-80380ba8a6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_word_vectorizer.fit(x_train.apply(lambda x: np.str_(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef743405-30df-45e4-844e-cceb54343809",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_tf_idf_word = tf_idf_word_vectorizer.transform(x_train.apply(lambda x: np.str_(x)))\n",
    "x_test_tf_idf_word = tf_idf_word_vectorizer.transform(x_test.apply(lambda x: np.str_(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "b5bd9343-f4e1-440a-832d-88593bf3015f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_absolutely', '_add_', '_after_', '_alicia', '_always_']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_word_vectorizer.get_feature_names()[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3acc5ee6-d621-4c29-b9a7-60f102a45b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(<426340x100315 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 14410624 stored elements in Compressed Sparse Row format>,\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(x_train_tf_idf_word)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d06446-70b9-427d-acd2-befb661ae2e7",
   "metadata": {},
   "source": [
    "#### charachter level n_gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c07afec7-b092-4210-a72d-c5f71a85cd9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 2))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 2))</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer(analyzer='char', ngram_range=(1, 2))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_chars_vectorizer = TfidfVectorizer(analyzer=\"char\", ngram_range=(1,2))\n",
    "tf_idf_chars_vectorizer.fit(x_train.apply(lambda x: np.str_(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1d08fd60-d506-46c1-a04c-93bc0da1767e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' _i', ' _k', ' _l', ' _m', ' _n']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_idf_chars_vectorizer.get_feature_names()[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69cdcc05-3ede-41b6-816c-ed4076c0d697",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_tf_idf_chars = tf_idf_chars_vectorizer.transform(x_train.apply(lambda x: np.str_(x)))\n",
    "x_test_tf_idf_chars = tf_idf_chars_vectorizer.transform(x_test.apply(lambda x: np.str_(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70e1e1c-0530-4a05-894e-3ade4347d7f7",
   "metadata": {},
   "source": [
    "### Export Vectorizers For Run Another Platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2024f106-aac5-43cc-b805-983fdfa446bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse, io"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a42447-573a-4df3-9da5-f07833650c76",
   "metadata": {},
   "source": [
    "#### Count Vectorizer Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec28259e-d7f4-4ee8-9f52-1f0eebda10b4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_train_count\", x_train_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3f847154-62b1-448f-be66-e1f5dfd6be02",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_test_count\", x_test_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e14b27-ec92-4d94-9741-b910beb1a8f0",
   "metadata": {},
   "source": [
    "#### TF-IDF Exports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fabc85-6478-4c9c-b656-6eb8c4b847c4",
   "metadata": {},
   "source": [
    "##### n_gram level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a486131-db46-4bf5-aa1a-1ccf77311cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_train_tf_idf_ngram\", x_train_tf_idf_ngram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9a358d1c-cefa-4db4-99af-9337ed9d71a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_test_tf_idf_ngram\", x_test_tf_idf_ngram)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f90a808-f2c6-4700-8513-2241c761c98d",
   "metadata": {},
   "source": [
    "##### word level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "424b8863-8e9b-4919-ae65-cf8443ed8d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_train_tf_idf_word\", x_train_tf_idf_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f82363d-fe04-45d7-9883-e35ca5229819",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_test_tf_idf_word\", x_test_tf_idf_word)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1772875b-b2e5-4cf1-905f-cb5c30d607ca",
   "metadata": {},
   "source": [
    "##### character level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "086d0663-c710-4a00-9fc6-44497b045630",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_train_tf_idf_chars\", x_train_tf_idf_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8ddc6b8-58a6-4bcc-9d19-61aa379d9514",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite(\"x_test_tf_idf_chars\", x_test_tf_idf_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9eaaa735-762d-4c73-92b3-bc902a32612a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import vectorizers\n",
    "#b = io.mmread('x_train_count.mtx')"
   ]
  }
 ],
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