text-representations.ipynb

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    "import pandas as pd \n",
    "import numpy as np\n",
    "import nltk\n",
    "import re \n",
    "import os \n",
    "import random \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
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    "<h1 style=\"text-align: center;\" class=\"list-group-item list-group-item-action active\">Table of Contents</h1>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href = \"#1\" role=\"tab\" aria-controls=\"settings\">1. Introduction<span class=\"badge badge-primary badge-pill\">1</span></a>\n",
    "<a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href = \"#2\" role=\"tab\" aria-controls=\"settings\">2. Load a Clean Dataset<span class=\"badge badge-primary badge-pill\">2</span></a>\n",
    "<a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#3\" role=\"tab\" aria-controls=\"settings\">3. Basic Text Pre-Processing<span class=\"badge badge-primary badge-pill\">3</span></a>\n",
    "   <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#4\" role=\"tab\" aria-controls=\"settings\">4. One-Hot Encoding<span class=\"badge badge-primary badge-pill\">4</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#5\" role=\"tab\" aria-controls=\"settings\">5. Bag of Words<span class=\"badge badge-primary badge-pill\">5</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#6\" role=\"tab\" aria-controls=\"settings\">6. Bag of N grams<span class=\"badge badge-primary badge-pill\">6</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#7\" role=\"tab\" aria-controls=\"settings\">7. TF-IDF<span class=\"badge badge-primary badge-pill\">7</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#8\" role=\"tab\" aria-controls=\"settings\">8. Word2vec Word Embeddings<span class=\"badge badge-primary badge-pill\">8</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#9\" role=\"tab\" aria-controls=\"settings\">9. Glove Word Embeddings<span class=\"badge badge-primary badge-pill\">9</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#10\" role=\"tab\" aria-controls=\"settings\">10. FastText Word Embeddings<span class=\"badge badge-primary badge-pill\">10</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\"  data-toggle=\"list\" href=\"#11\" role=\"tab\" aria-controls=\"settings\">11.   Visualizing Embeddings<span class=\"badge badge-primary badge-pill\">11</span></a>\n",
    "  \n"
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    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Introduction</h1><a id = \"1\" ></a>\n",
    "\n",
    "\n",
    "In Natural Language Processing (NLP) the conversion of raw-text to numerical form is called <b>Text Representation</b> and believe me this step is one of the most important steps in the NLP pipeline as if we feed in poor features in ML Model, we will get poor results. In computer science, this is often called “garbage in, garbage out.”\n",
    "\n",
    "<b>I observed in NLP feeding a good text representation to an ordinary algorithm will get you much farther compared to applying a topnotch algorithm to an ordinary text representation.</b>\n",
    "\n",
    "In this notebook, I will discuss various text-representation schemes with their advantages and disadvantages so that you can choose one of the schemes which suit your task most. Our main objective is to transform a given text into numerical form so that it can be fed\n",
    "into NLP and ML algorithms.\n",
    "\n",
    "![](https://www.oreilly.com/library/view/practical-natural-language/9781492054047/assets/pnlp_0301.png)\n",
    "\n",
    "In this notebook, the focus will be on the dotted box in the figure \n",
    "\n",
    "\n",
    "here write a para on the flow of the notebook later\n"
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    "But before moving on to the Text representation step first we have to get a cleaned dataset which then has to be preprocessed. In this notebook, I will be using only a few basic steps to preprocess the text data"
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    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Load a Clean Dataset</h1><a id = \"2\" ></a>\n",
    "\n",
    "Kaggle Datasets is one of the best sources to get a clean dataset for this notebook I will be using [Twitter US Airline Sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment) dataset.\n"
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    "clean_data = pd.read_csv('../input/twitter-airline-sentiment/Tweets.csv')"
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       "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
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       "2  570301083672813571           neutral                        0.6837   \n",
       "3  570301031407624196          negative                        1.0000   \n",
       "4  570300817074462722          negative                        1.0000   \n",
       "\n",
       "  negativereason  negativereason_confidence         airline  \\\n",
       "0            NaN                        NaN  Virgin America   \n",
       "1            NaN                     0.0000  Virgin America   \n",
       "2            NaN                        NaN  Virgin America   \n",
       "3     Bad Flight                     0.7033  Virgin America   \n",
       "4     Can't Tell                     1.0000  Virgin America   \n",
       "\n",
       "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
       "0                    NaN     cairdin                 NaN              0   \n",
       "1                    NaN    jnardino                 NaN              0   \n",
       "2                    NaN  yvonnalynn                 NaN              0   \n",
       "3                    NaN    jnardino                 NaN              0   \n",
       "4                    NaN    jnardino                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
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      "RangeIndex: 14640 entries, 0 to 14639\n",
      "Data columns (total 15 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   tweet_id                      14640 non-null  int64  \n",
      " 1   airline_sentiment             14640 non-null  object \n",
      " 2   airline_sentiment_confidence  14640 non-null  float64\n",
      " 3   negativereason                9178 non-null   object \n",
      " 4   negativereason_confidence     10522 non-null  float64\n",
      " 5   airline                       14640 non-null  object \n",
      " 6   airline_sentiment_gold        40 non-null     object \n",
      " 7   name                          14640 non-null  object \n",
      " 8   negativereason_gold           32 non-null     object \n",
      " 9   retweet_count                 14640 non-null  int64  \n",
      " 10  text                          14640 non-null  object \n",
      " 11  tweet_coord                   1019 non-null   object \n",
      " 12  tweet_created                 14640 non-null  object \n",
      " 13  tweet_location                9907 non-null   object \n",
      " 14  user_timezone                 9820 non-null   object \n",
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       "<AxesSubplot:xlabel='airline_sentiment', ylabel='count'>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"airline_sentiment\", data = clean_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "849e4b1e",
   "metadata": {
    "papermill": {
     "duration": 0.072146,
     "end_time": "2021-12-07T14:22:11.116201",
     "exception": false,
     "start_time": "2021-12-07T14:22:11.044055",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Basic Text Pre-Processing</h1><a id = \"3\" ></a>\n",
    "\n",
    "Text preprocessing steps include a few essential tasks to further clean the available text data. It includes tasks like:-\n",
    "\n",
    "**1. Stop-Word Removal** : In English words like a, an, the, as, in, on, etc. are considered as stop-words so according to our requirements we can remove them to reduce vocabulary size as these words don't have some specific meaning\n",
    "\n",
    "**2. Lower Casing** : Convert all words into the lower case because the upper or lower case may not make a difference for the problem.\n",
    "And we are reducing vocabulary size by doing so. \n",
    "\n",
    "**3. Stemming** : Stemming refers to the process of removing suffixes and reducing a word to some base form such that all different variants of that word can be represented by the same form (e.g., “walk” and “walking” are both reduced to “walk”).\n",
    "\n",
    "**4. Tokenization** : NLP software typically analyzes text by breaking it up into words (tokens) and sentences.\n",
    "\n",
    "Pre-processing of the text is not the main objective of this notebook that's why I am just covering a few basic steps in a brief\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04744cce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:11.266128Z",
     "iopub.status.busy": "2021-12-07T14:22:11.265361Z",
     "iopub.status.idle": "2021-12-07T14:22:11.267682Z",
     "shell.execute_reply": "2021-12-07T14:22:11.268298Z",
     "shell.execute_reply.started": "2021-12-07T13:17:18.964893Z"
    },
    "papermill": {
     "duration": 0.080846,
     "end_time": "2021-12-07T14:22:11.268478",
     "exception": false,
     "start_time": "2021-12-07T14:22:11.187632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# First of all let's drop the columns which we don't required\n",
    "\n",
    "waste_col = ['tweet_id', 'airline_sentiment_confidence',\n",
    "       'negativereason', 'negativereason_confidence', 'airline',\n",
    "       'airline_sentiment_gold', 'name', 'negativereason_gold',\n",
    "       'retweet_count', 'tweet_coord', 'tweet_created',\n",
    "       'tweet_location', 'user_timezone']\n",
    "\n",
    "data = clean_data.drop(waste_col, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76ae080b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:11.418865Z",
     "iopub.status.busy": "2021-12-07T14:22:11.418160Z",
     "iopub.status.idle": "2021-12-07T14:22:11.421007Z",
     "shell.execute_reply": "2021-12-07T14:22:11.421498Z",
     "shell.execute_reply.started": "2021-12-07T13:17:18.972057Z"
    },
    "papermill": {
     "duration": 0.082756,
     "end_time": "2021-12-07T14:22:11.421681",
     "exception": false,
     "start_time": "2021-12-07T14:22:11.338925",
     "status": "completed"
    },
    "tags": []
   },
   "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>airline_sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  airline_sentiment                                               text\n",
       "0           neutral                @VirginAmerica What @dhepburn said.\n",
       "1          positive  @VirginAmerica plus you've added commercials t...\n",
       "2           neutral  @VirginAmerica I didn't today... Must mean I n...\n",
       "3          negative  @VirginAmerica it's really aggressive to blast...\n",
       "4          negative  @VirginAmerica and it's a really big bad thing..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76460d5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:11.569737Z",
     "iopub.status.busy": "2021-12-07T14:22:11.568997Z",
     "iopub.status.idle": "2021-12-07T14:22:11.571009Z",
     "shell.execute_reply": "2021-12-07T14:22:11.571526Z",
     "shell.execute_reply.started": "2021-12-07T13:17:18.992147Z"
    },
    "papermill": {
     "duration": 0.079454,
     "end_time": "2021-12-07T14:22:11.571716",
     "exception": false,
     "start_time": "2021-12-07T14:22:11.492262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def sentiment(x):\n",
    "    if x == 'positive':\n",
    "        return 1\n",
    "    elif x == 'negative':\n",
    "        return -1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bff4f92b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:11.717874Z",
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     "shell.execute_reply": "2021-12-07T14:22:11.853330Z",
     "shell.execute_reply.started": "2021-12-07T13:17:19.000278Z"
    },
    "papermill": {
     "duration": 0.210989,
     "end_time": "2021-12-07T14:22:11.853570",
     "exception": false,
     "start_time": "2021-12-07T14:22:11.642581",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer \n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "stopwords = stopwords.words('english')\n",
    "stemmer = SnowballStemmer('english')\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "# As this dataset is fetched from twitter so it has lots of people tag in tweets\n",
    "# we will remove them \n",
    "tags = r\"@\\w*\"\n",
    "\n",
    "\n",
    "def preprocess_text(sentence, stem = False):\n",
    "    \n",
    "    sentence = [re.sub(tags, \"\", sentence)]\n",
    "    text = []\n",
    "    for word in sentence:\n",
    "        \n",
    "        if word not in stopwords:\n",
    "            \n",
    "            if stem:\n",
    "                text.append(stemmer.stem(word).lower())\n",
    "            else:\n",
    "                text.append(word.lower())\n",
    "    return tokenizer.tokenize(\" \".join(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78a9e697",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:12.004808Z",
     "iopub.status.busy": "2021-12-07T14:22:12.004036Z",
     "iopub.status.idle": "2021-12-07T14:22:12.008748Z",
     "shell.execute_reply": "2021-12-07T14:22:12.009714Z",
     "shell.execute_reply.started": "2021-12-07T13:17:19.103723Z"
    },
    "papermill": {
     "duration": 0.082926,
     "end_time": "2021-12-07T14:22:12.010037",
     "exception": false,
     "start_time": "2021-12-07T14:22:11.927111",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orignal Text : @VirginAmerica I &lt;3 pretty graphics. so much better than minimal iconography. :D\n",
      "\n",
      "Preprocessed Text : ['i', 'lt', '3', 'pretty', 'graphics', 'so', 'much', 'better', 'than', 'minimal', 'iconography', 'd']\n"
     ]
    }
   ],
   "source": [
    "print(f\"Orignal Text : {data.text[11]}\")\n",
    "print()\n",
    "print(f\"Preprocessed Text : {preprocess_text(data.text[11])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "805d5248",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:12.161539Z",
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     "iopub.status.idle": "2021-12-07T14:22:12.371282Z",
     "shell.execute_reply": "2021-12-07T14:22:12.371809Z",
     "shell.execute_reply.started": "2021-12-07T13:17:19.112119Z"
    },
    "papermill": {
     "duration": 0.286869,
     "end_time": "2021-12-07T14:22:12.372021",
     "exception": false,
     "start_time": "2021-12-07T14:22:12.085152",
     "status": "completed"
    },
    "tags": []
   },
   "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>airline_sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>[what, said]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>[plus, you, ve, added, commercials, to, the, e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutral</td>\n",
       "      <td>[i, didn, t, today, must, mean, i, need, to, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>negative</td>\n",
       "      <td>[it, s, really, aggressive, to, blast, obnoxio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>negative</td>\n",
       "      <td>[and, it, s, a, really, big, bad, thing, about...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  airline_sentiment                                               text\n",
       "0           neutral                                       [what, said]\n",
       "1          positive  [plus, you, ve, added, commercials, to, the, e...\n",
       "2           neutral  [i, didn, t, today, must, mean, i, need, to, t...\n",
       "3          negative  [it, s, really, aggressive, to, blast, obnoxio...\n",
       "4          negative  [and, it, s, a, really, big, bad, thing, about..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.text = data.text.map(preprocess_text)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d801a4c",
   "metadata": {
    "papermill": {
     "duration": 0.07431,
     "end_time": "2021-12-07T14:22:12.520458",
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     "start_time": "2021-12-07T14:22:12.446148",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now we have preprocessed textual data so now we can proceed further in this notebook and discuss various text representation approaches in detail"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f9edb0b",
   "metadata": {
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    "tags": []
   },
   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">One-Hot Encoding</h1><a id = \"4\" ></a>\n",
    "\n",
    "\n",
    "In one-hot encoding, each word w in the corpus vocabulary is given a unique integer ID (wid) that is between 1 and |V|, where V is the set of the corpus vocabulary. Each word is then represented by a V dimensional binary vector of 0s and 1s. This is done via a |V| dimension vector filled with all 0s barring the index, where index = wid. At this index, we simply put a 1. The representation for individual words is then combined to form a sentence representation.\n",
    "\n",
    "Consider an Example \n",
    "\n",
    "![](https://miro.medium.com/max/886/1*_da_YknoUuryRheNS-SYWQ.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09b46ee1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:12.818949Z",
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     "shell.execute_reply": "2021-12-07T14:22:12.822317Z",
     "shell.execute_reply.started": "2021-12-07T13:17:19.468086Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#this is an example vocabulary just to make concept clear\n",
    "sample_vocab = ['the', 'cat', 'sat', 'on', 'mat', 'dog', 'run', 'green', 'tree']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d5c360d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:12.970010Z",
     "iopub.status.busy": "2021-12-07T14:22:12.969337Z",
     "iopub.status.idle": "2021-12-07T14:22:17.704052Z",
     "shell.execute_reply": "2021-12-07T14:22:17.703010Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# vocabulary of words present in dataset\n",
    "data_vocab = []\n",
    "for text in data.text:\n",
    "    for word in text:\n",
    "        if word not in data_vocab:\n",
    "            data_vocab.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6920feb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:17.871363Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One Hot Representation for sentence \"the cat sat on the mat\" :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       " [1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       " [0, 0, 0, 1, 0, 0, 0, 0, 0]]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#function to return one-hot representation of passed text\n",
    "def get_onehot_representation(text, vocab = data_vocab):\n",
    "    onehot_encoded = []\n",
    "    for word in text:\n",
    "        temp = [0]*len(vocab)\n",
    "        temp[vocab.index(word)-1] = 1\n",
    "        onehot_encoded.append(temp)\n",
    "    return onehot_encoded\n",
    "\n",
    "print(\"One Hot Representation for sentence \\\"the cat sat on the mat\\\" :\")\n",
    "get_onehot_representation(['the', 'cat', 'sat', 'on', 'the', 'mat'], sample_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3c6f31d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:18.027795Z",
     "iopub.status.busy": "2021-12-07T14:22:18.027074Z",
     "iopub.status.idle": "2021-12-07T14:22:18.030188Z",
     "shell.execute_reply": "2021-12-07T14:22:18.030685Z",
     "shell.execute_reply.started": "2021-12-07T13:17:24.224048Z"
    },
    "papermill": {
     "duration": 0.082567,
     "end_time": "2021-12-07T14:22:18.030870",
     "exception": false,
     "start_time": "2021-12-07T14:22:17.948303",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Vocabulary : 14276\n",
      "Sample of Vocabulary : ['number', 'awaiting', 'return', 'phone', 'call', 'prefer', 'use', 'self', 'service', 'option']\n"
     ]
    }
   ],
   "source": [
    "print(f'Length of Vocabulary : {len(data_vocab)}')\n",
    "print(f'Sample of Vocabulary : {data_vocab[302 : 312]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9e8ab0",
   "metadata": {
    "papermill": {
     "duration": 0.07211,
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     "exception": false,
     "start_time": "2021-12-07T14:22:18.103671",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We have 14276 different words in a given dataset thus this implies each word representation for one-hot encoding schema will be of 14276-dimensional vector mark that this much big representation is just for a single word if we consider the representation of a sentence which consist of let say 20 words in it then it will be represented with (20,14276) sized matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ae0d3ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:18.371550Z",
     "iopub.status.busy": "2021-12-07T14:22:18.370111Z",
     "iopub.status.idle": "2021-12-07T14:22:18.375456Z",
     "shell.execute_reply": "2021-12-07T14:22:18.375914Z",
     "shell.execute_reply.started": "2021-12-07T13:17:24.240142Z"
    },
    "papermill": {
     "duration": 0.12788,
     "end_time": "2021-12-07T14:22:18.376127",
     "exception": false,
     "start_time": "2021-12-07T14:22:18.248247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes of a single sentence : (15, 14276)\n"
     ]
    }
   ],
   "source": [
    "sample_one_hot_rep = get_onehot_representation(data.text[7], data_vocab)\n",
    "print(f\"Shapes of a single sentence : {np.array(sample_one_hot_rep).shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d00e7b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:18.526924Z",
     "iopub.status.busy": "2021-12-07T14:22:18.525529Z",
     "iopub.status.idle": "2021-12-07T14:22:18.529338Z",
     "shell.execute_reply": "2021-12-07T14:22:18.528715Z",
     "shell.execute_reply.started": "2021-12-07T13:17:24.298219Z"
    },
    "papermill": {
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     "end_time": "2021-12-07T14:22:18.529486",
     "exception": false,
     "start_time": "2021-12-07T14:22:18.449126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#one-hot representation for dataset sentences \n",
    "\n",
    "# data.loc[:, 'one_hot_rep'] = data.loc[:, 'text'].map(get_onehot_representation)\n",
    "\n",
    "#if you run this cell it will give you a memory error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea0130d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:22:18.686726Z",
     "iopub.status.busy": "2021-12-07T14:22:18.685615Z",
     "iopub.status.idle": "2021-12-07T14:22:18.690158Z",
     "shell.execute_reply": "2021-12-07T14:22:18.689593Z",
     "shell.execute_reply.started": "2021-12-07T13:17:24.311020Z"
    },
    "papermill": {
     "duration": 0.087987,
     "end_time": "2021-12-07T14:22:18.690306",
     "exception": false,
     "start_time": "2021-12-07T14:22:18.602319",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\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>airline_sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>[what, said]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>[plus, you, ve, added, commercials, to, the, e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutral</td>\n",
       "      <td>[i, didn, t, today, must, mean, i, need, to, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>negative</td>\n",
       "      <td>[it, s, really, aggressive, to, blast, obnoxio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>negative</td>\n",
       "      <td>[and, it, s, a, really, big, bad, thing, about...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  airline_sentiment                                               text\n",
       "0           neutral                                       [what, said]\n",
       "1          positive  [plus, you, ve, added, commercials, to, the, e...\n",
       "2           neutral  [i, didn, t, today, must, mean, i, need, to, t...\n",
       "3          negative  [it, s, really, aggressive, to, blast, obnoxio...\n",
       "4          negative  [and, it, s, a, really, big, bad, thing, about..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c092a14",
   "metadata": {
    "papermill": {
     "duration": 0.073998,
     "end_time": "2021-12-07T14:22:18.838243",
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     "start_time": "2021-12-07T14:22:18.764245",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "One-hot encoding is intuitive to understand and straightforward to implement. However, it has lots of disadvantages listed below\n",
    "\n",
    "1. The size of a one-hot vector is directly proportional to the size of the vocabulary and if we consider a real-world vocabulary size it may be in millions so we can not represent a single word with a million-dimensional vector. \n",
    "\n",
    "2. One-hot representation does not give a fixed-length representation for text, i.e., the sentence with 32 words in it and 40 words in it has variable length representation. But for most learning algorithms, we need the feature vectors to be of the same length.\n",
    "\n",
    "3. One-Hot representation gives each word the same weight whether that word is important for the task or not.\n",
    "\n",
    "4. One-Hot representation does not represent the meaning of the word in a proper numerical manner as embedding vectors do. Consider an example word read, reading should have similar real-valued vector representation but in this case, they have different representations. \n",
    "\n",
    "5. Let say we train the model on some article and get the vocabulary of size 10000 but what if we use this vocabulary on that text which contains words that are not present in learned vocabulary. This is Known as **Out Of Vocabulary (OOV)** problem.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f2b43c7",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Bag of words</h1><a id = \"5\" ></a>\n",
    "\n",
    "Bag of words (BoW) is a classical text representation technique that has been used commonly in NLP, especially in text classification problems. The key idea behind it is as follows: represent the text under consideration as a bag (collection) of words while ignoring the order and context.\n",
    "\n",
    "Similar to one-hot encoding, BoW maps words to unique integer IDs between 1 and |V|. Each document in the corpus is then converted into a vector of |V| dimensions were in the ith component of the vector, i = wid, is simply the number of times the word w occurs in the document, i.e., we simply score each word in V by their occurrence count in the document.\n",
    "\n",
    "Consider an example:\n",
    "\n",
    "let say we have a vocabulary **V consisting of words --> {the, cat, sat, in, hat, with}** then the bag of word representation of a few sentences will be given as \n",
    "\n",
    "![](https://miro.medium.com/max/1400/1*3IACMnNpwVlCl8kSTJocPA.png)"
   ]
  },
  {
   "cell_type": "code",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary mapping for given sample corpus : \n",
      " {'the': 4, 'cat': 0, 'sat': 3, 'in': 2, 'hat': 1, 'with': 5}\n",
      "\n",
      "Bag of word Representation of sentence 'the cat cat sat in the hat'\n",
      "[[2 1 1 1 2 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "sample_bow = CountVectorizer()\n",
    "\n",
    "# sample_corpus = [['the', 'cat', 'sat'], \n",
    "#                  ['the', 'cat', 'sat', 'in', 'the', 'hat'],\n",
    "#                  ['the', 'cat', 'with', 'the', 'hat']]\n",
    "\n",
    "sample_corpus = [\"the cat sat\", \"the cat sat in the hat\", \"the cat with the hat\"]\n",
    "\n",
    "sample_bow.fit(sample_corpus)\n",
    "\n",
    "def get_bow_representation(text):\n",
    "        return sample_bow.transform(text)\n",
    "    \n",
    "print(f\"Vocabulary mapping for given sample corpus : \\n {sample_bow.vocabulary_}\")\n",
    "print(\"\\nBag of word Representation of sentence 'the cat cat sat in the hat'\")\n",
    "print(get_bow_representation([\"the cat cat sat in the hat\"]).toarray())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8d12d8",
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   "source": [
    "Sometimes, we don’t care about the frequency of occurrence of words in the text and we only want to represent whether a word exists in the text or not. In such cases, we just initialize CountVectorizer with the binary=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e84d3f35",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vacabulary mapping for given sample corpus : \n",
      " {'the': 4, 'cat': 0, 'sat': 3, 'in': 2, 'hat': 1, 'with': 5}\n",
      "\n",
      "Bag of word Representation of sentence 'the the the the cat cat sat in the hat'\n",
      "[[1 1 1 1 1 0]]\n"
     ]
    }
   ],
   "source": [
    "sample_bow = CountVectorizer(binary = True)\n",
    "\n",
    "sample_corpus = [\"the cat sat\", \"the cat sat in the hat\", \"the cat with the hat\"]\n",
    "\n",
    "sample_bow.fit(sample_corpus)\n",
    "\n",
    "def get_bow_representation(text):\n",
    "        return sample_bow.transform(text)\n",
    "    \n",
    "print(f\"Vacabulary mapping for given sample corpus : \\n {sample_bow.vocabulary_}\")\n",
    "print(\"\\nBag of word Representation of sentence 'the the the the cat cat sat in the hat'\")\n",
    "print(get_bow_representation([\"the the the the cat cat sat in the hat\"]).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1823875",
   "metadata": {
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   "source": [
    "# generate bag of word representation for given dataset\n",
    "\n",
    "bow = CountVectorizer()\n",
    "bow_rep = bow.fit_transform(data.loc[:, 'text'].astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aed2bd97",
   "metadata": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "# intrested one can see vocabulary of given corpus by uncommenting below code line\n",
    "\n",
    "# bow.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dc7383a5",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Bag of word representaion matrix : (14640, 14238)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape of Bag of word representaion matrix : {bow_rep.toarray().shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aac8045",
   "metadata": {
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     "status": "completed"
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   },
   "source": [
    "As we know One-hot representation does not give a fixed-length representation for text but from the output of the above cell, we can interpret that bag of the word has a fixed-length vector representation (14238 dimensional) for each word but it is too big again in this case also."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e0e573",
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   "source": [
    "**Advantages of this Bag of words(BoW) encoding** :\n",
    "\n",
    "1. Like one-hot encoding, BoW is fairly simple to understand and implement.\n",
    "\n",
    "2. With this representation, documents having the same words will have their vector representations closer to each other in Euclidean space as compared to documents with completely different words.\n",
    "\n",
    "    Consider an example Where \n",
    "\n",
    "    S1 = \"cat on the mat\" --> BoW Representation --> {0 1 1 0 1 0 1} <br>\n",
    "    S2 = \"mat on the cat\" --> BoW Representation --> {0 1 1 0 1 0 1} <br>\n",
    "    S3 = \"dog in the mat\" --> BoW Representation --> {0 1 0 1 1 1 0} <br>\n",
    "\n",
    "    The distance between S1 and S2 is 0 as compared to the distance between S1 and S3, which is 2. Thus, the vector space resulting from the BoW scheme captures the semantic similarity of documents. So if two documents have a similar vocabulary, they’ll be closer to each other in the vector space and vice versa.\n",
    "\n",
    "3. We have a fixed-length encoding for any sentence of arbitrary length.\n",
    "\n",
    "**Disadvantages of this Bag of words(BoW) encoding** :\n",
    "\n",
    "1. The size of the vector increases with the size of the vocabulary as in our case it is 14238 dimensional. Thus, sparsity continues to be a problem. One way to control it is by limiting the vocabulary to n number of the most frequent words.\n",
    "\n",
    "2. It does not capture the similarity between different words that mean the same thing. Say we have three documents: “walk”, “walked”, and “walking”. BoW vectors of all three documents will be equally apart.\n",
    "\n",
    "3. This representation does not have any way to handle **out of vocabulary (OOV)** words (i.e., new words that were not seen in the corpus that was used to build the vectorizer).\n",
    "\n",
    "4. As the name indicates, it is a “bag” of words—word order information is lost in this representation. Both S1 and S2 will have the same representation in this scheme.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be49b0ec",
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   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Bag of N-Grams</h1><a id = \"6\" ></a>\n",
    "\n",
    "All the representation schemes we’ve seen so far treat words as independent units. There is no notion of phrases or word order. The bag-of-n-grams (BoN) approach tries to remedy this. It does so by breaking text into chunks of n contiguous words (or tokens). This can help us capture some context, which earlier approaches could not do. Each chunk is called an n-gram.\n",
    "\n",
    "**One can simply say Bag of words (BoW) is a special case of the Bag of n-grams having n = 1.**\n",
    "\n",
    "The corpus vocabulary, V, is then nothing but a collection of all unique n-grams across the text corpus. Then, each document in the corpus is represented by a vector of length |V|. This vector simply contains the frequency counts of n-grams present in the document and zero for the n-grams that are not present.\n",
    "\n",
    "Consider an Example: \n",
    "\n",
    "![](https://i.stack.imgur.com/8ARA1.png)\n",
    "\n",
    "\n",
    "The following code cell shows an example of a BoN representation considering 1–3 n-gram word features to represent the corpus that we’ve used so far.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unigram Vocabulary mapping for given sample corpus : \n",
      " {'the': 4, 'cat': 0, 'sat': 3, 'in': 2, 'hat': 1, 'with': 5}\n",
      "\n",
      "Bag of 1-gram (unigram) Representation of sentence 'the cat cat sat in the hat'\n",
      "[[2 1 1 1 2 0]]\n"
     ]
    }
   ],
   "source": [
    "# Bag of 1-gram (unigram)\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "sample_boN = CountVectorizer(ngram_range = (1, 1))\n",
    "\n",
    "sample_corpus = [\"the cat sat\", \"the cat sat in the hat\", \"the cat with the hat\"]\n",
    "\n",
    "sample_boN.fit(sample_corpus)\n",
    "\n",
    "def get_boN_representation(text):\n",
    "        return sample_boN.transform(text)\n",
    "    \n",
    "print(f\"Unigram Vocabulary mapping for given sample corpus : \\n {sample_boN.vocabulary_}\")\n",
    "print(\"\\nBag of 1-gram (unigram) Representation of sentence 'the cat cat sat in the hat'\")\n",
    "print(get_boN_representation([\"the cat cat sat in the hat\"]).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72339ae4",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bigram Vocabulary mapping for given sample corpus : \n",
      " {'the cat': 4, 'cat sat': 0, 'sat in': 3, 'in the': 2, 'the hat': 5, 'cat with': 1, 'with the': 6}\n",
      "\n",
      "Bag of 2-gram (bigram) Representation of sentence 'the cat cat sat in the hat'\n",
      "[[1 0 1 1 1 1 0]]\n"
     ]
    }
   ],
   "source": [
    "# Bag of 2-gram (bigram)\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "sample_boN = CountVectorizer(ngram_range = (2, 2))\n",
    "\n",
    "sample_corpus = [\"the cat sat\", \"the cat sat in the hat\", \"the cat with the hat\"]\n",
    "\n",
    "sample_boN.fit(sample_corpus)\n",
    "\n",
    "def get_boN_representation(text):\n",
    "        return sample_boN.transform(text)\n",
    "    \n",
    "print(f\"Bigram Vocabulary mapping for given sample corpus : \\n {sample_boN.vocabulary_}\")\n",
    "print(\"\\nBag of 2-gram (bigram) Representation of sentence 'the cat cat sat in the hat'\")\n",
    "print(get_boN_representation([\"the cat cat sat in the hat\"]).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c0d0129e",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trigram Vocabulary mapping for given sample corpus : \n",
      " {'the cat sat': 4, 'cat sat in': 0, 'sat in the': 3, 'in the hat': 2, 'the cat with': 5, 'cat with the': 1, 'with the hat': 6}\n",
      "\n",
      "Bag of 3-gram (trigram) Representation of sentence 'the cat cat sat in the hat'\n",
      "[[1 0 1 1 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "# Bag of 3-gram (trigram)\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "sample_boN = CountVectorizer(ngram_range = (3, 3))\n",
    "\n",
    "sample_corpus = [\"the cat sat\", \"the cat sat in the hat\", \"the cat with the hat\"]\n",
    "\n",
    "sample_boN.fit(sample_corpus)\n",
    "\n",
    "def get_boN_representation(text):\n",
    "        return sample_boN.transform(text)\n",
    "    \n",
    "print(f\"Trigram Vocabulary mapping for given sample corpus : \\n {sample_boN.vocabulary_}\")\n",
    "print(\"\\nBag of 3-gram (trigram) Representation of sentence 'the cat cat sat in the hat'\")\n",
    "print(get_boN_representation([\"the cat cat sat in the hat\"]).toarray())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cafa10d",
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   "source": [
    "**Here are the main advantages and disadvantages of BoN Representation:**\n",
    "\n",
    "1. It captures some context and word-order information in the form of n-grams.\n",
    "\n",
    "2. Thus, the resulting vector space can capture some semantic similarity. Documents having the same n-grams will have their vectors closer to each other in Euclidean space as compared to documents with completely different n-grams.\n",
    "\n",
    "3. As n increases, dimensionality (and therefore sparsity) only increases rapidly.\n",
    "\n",
    "4. It still provides no way to address the **out of vocabulary(OOV)** problem."
   ]
  },
  {
   "attachments": {
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "2c2d4709",
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   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">TF-IDF</h1><a id = \"7\" ></a>\n",
    "\n",
    "\n",
    "In all the three approaches we’ve seen so far, all the words in the text are treated as equally important—there’s no notion of some words in the document being more important than others. TF-IDF, or term frequency-inverse document frequency, addresses this issue. It aims to quantify the importance of a given word relative to other words in the document and in the corpus.\n",
    "\n",
    "The intuition behind TF-IDF is as follows: if a word w appears many times in a sentence S1 but does not occur much in the rest of the Sentences Sn in the corpus, then the word w must be of great importance to the Sentence S1. The importance of w should increase in proportion to its frequency in S1 (how many times that word occurs in sentence S1), but at the same time, its importance should decrease in proportion to the word’s frequency in other Sentence Sn in the corpus. **Mathematically, this is captured using two quantities: TF and IDF. The two are then multiplied to arrive at the TF-IDF score.**\n",
    "\n",
    "**TF (term frequency) measures how often a term or word occurs in a given document.**\n",
    "\n",
    "Mathematical Expression of TF\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "**IDF (inverse document frequency)** measures the importance of the term across a corpus. In computing TF, all terms are given equal importance (weightage). However, it’s a well-known fact that stop words like is, are, am, etc., are not important, even though they occur frequently. To account for such cases, IDF weighs down the terms that are very common across a corpus and weighs up the rare terms. IDF of a term t is calculated as follows:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "The TF-IDF score is a product of these two terms. Thus, TF-IDF score = TF * IDF. Let’s consider an example.\n",
    "\n",
    "Sentence A = The Car is Driven on the Road <br>\n",
    "Sentence B = The Truck is Driven on the highway <br>\n",
    "\n",
    "Computation of TF-IDF scores are shown below\n",
    "\n",
    "![](https://cdn-media-1.freecodecamp.org/images/1*q3qYevXqQOjJf6Pwdlx8Mw.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "decfeeec",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDF Values for sample corpus : [1.         1.28768207 1.69314718 1.28768207 1.         1.69314718]\n",
      "TF-IDF Representation for sentence 'the cat sat in the hat' :\n",
      "[[0.29903422 0.385061   0.50630894 0.385061   0.59806843 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf = TfidfVectorizer()\n",
    "\n",
    "sample_corpus = [\"the cat sat\", \"the cat sat in the hat\", \"the cat with the hat\"]\n",
    "tfidf_rep = tfidf.fit_transform(sample_corpus)\n",
    "print(f\"IDF Values for sample corpus : {tfidf.idf_}\")\n",
    "\n",
    "\n",
    "print(\"TF-IDF Representation for sentence 'the cat sat in the hat' :\") \n",
    "print(tfidf.transform([\"the cat sat in the hat\"]).toarray())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58840a00",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Similar to BoW, we can use the TF-IDF vectors to calculate the similarity between two texts using a similarity measure like Euclidean distance or cosine similarity. TF-IDF is a commonly used representation in application scenarios such as information\n",
    "retrieval and text classification. However, even though TF-IDF is better than the vectorization methods we saw earlier in terms of capturing similarities between words, **it still suffers from the curse of high dimensionality.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c38a382",
   "metadata": {
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   "source": [
    "**Here are the main advantages and disadvantages of TF-IDF Representation:**\n",
    "\n",
    "1. Its Implementation is not that easy as compared to techniques discussed above \n",
    "2. We have a fixed-length encoding for any sentence of arbitrary length.\n",
    "3. The feature vectors are high-dimensional representations. The dimensionality increases with the size of the vocabulary.\n",
    "4. It did capture a bit of the semantics of the sentence. \n",
    "5. They too cannot handle OOV words.\n",
    "\n",
    "With this, we come to the end of basic vectorization approaches. Now, let’s start looking at distributed representations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff66412",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Word2vec Word Embeddings</h1><a id = \"8\" ></a>\n",
    "\n",
    "**Word Embeddings** : They are a real-valued vector representation of words that allows words with the same meaning to have similar representation. Thus we can say word embeddings are the projection of meanings of words in a real-valued vector \n",
    "\n",
    "Word2vec is a Word Embedding Technique published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text.\n",
    "\n",
    "It is the representation of words that allows words with the same meaning to have similar representation, Word2vec operationalizes this by projecting the meaning of the words in a vector space where words with similar meanings will tend to cluster together, and works with very different meanings are far from one another.\n",
    "\n",
    "**Using Pre-trained word2vec word embeddings** <br>\n",
    "Training your own word embeddings is a pretty expensive process (in terms of both time and computing). Thankfully, for many scenarios, it’s not necessary to train your own embeddings Someone has done the hard work of training word embeddings on a large corpus, such as Wikipedia, news articles, or even the entire web, and has put words and their corresponding vectors on the web. These embeddings\n",
    "can be downloaded and used to get the vectors for the words you want.  \n",
    "\n",
    "Some of the most popular pre-trained embeddings are Word2vec by Google, GloVe by Stanford, and fasttext embeddings by Facebook, to name a few.\n",
    "\n",
    "Below code, cell demonstrates how to use pre-trained word2vec word embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b88ceca6",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec, KeyedVectors\n",
    "pretrained_path = \"../input/googles-trained-word2vec-model-in-python/GoogleNews-vectors-negative300.bin\"\n",
    "\n",
    "Word2VecModel = KeyedVectors.load_word2vec_format(pretrained_path, binary = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb37176",
   "metadata": {
    "papermill": {
     "duration": 0.07589,
     "end_time": "2021-12-07T14:23:39.298193",
     "exception": false,
     "start_time": "2021-12-07T14:23:39.222303",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we learned Word2vec do have a similar vector representation for words with the same meaning so let's check the similar words for the word \"good\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "687a5264",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:23:39.456724Z",
     "iopub.status.busy": "2021-12-07T14:23:39.456027Z",
     "iopub.status.idle": "2021-12-07T14:23:42.585042Z",
     "shell.execute_reply": "2021-12-07T14:23:42.586729Z",
     "shell.execute_reply.started": "2021-12-07T13:18:39.625000Z"
    },
    "papermill": {
     "duration": 3.212874,
     "end_time": "2021-12-07T14:23:42.587205",
     "exception": false,
     "start_time": "2021-12-07T14:23:39.374331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('great', 0.7291510105133057), ('bad', 0.7190051078796387), ('terrific', 0.6889115571975708), ('decent', 0.6837348341941833), ('nice', 0.6836092472076416), ('excellent', 0.644292950630188), ('fantastic', 0.6407778263092041), ('better', 0.6120728850364685), ('solid', 0.5806034803390503), ('lousy', 0.576420247554779)]\n"
     ]
    }
   ],
   "source": [
    "print(Word2VecModel.most_similar('good'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6c5debc1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:23:42.813344Z",
     "iopub.status.busy": "2021-12-07T14:23:42.812394Z",
     "iopub.status.idle": "2021-12-07T14:23:42.816473Z",
     "shell.execute_reply": "2021-12-07T14:23:42.817001Z",
     "shell.execute_reply.started": "2021-12-07T13:18:42.030295Z"
    },
    "papermill": {
     "duration": 0.142583,
     "end_time": "2021-12-07T14:23:42.817179",
     "exception": false,
     "start_time": "2021-12-07T14:23:42.674596",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.04052734  0.0625     -0.01745605  0.07861328  0.03271484 -0.01263428\n",
      "  0.00964355  0.12353516 -0.02148438  0.15234375 -0.05834961 -0.10644531\n",
      "  0.02124023  0.13574219 -0.13183594  0.17675781  0.27148438  0.13769531\n",
      " -0.17382812 -0.14160156 -0.03076172  0.19628906 -0.03295898  0.125\n",
      "  0.25390625  0.12695312 -0.15234375  0.03198242  0.01135254 -0.01361084\n",
      " -0.12890625  0.01019287  0.23925781 -0.08447266  0.140625    0.13085938\n",
      " -0.04516602  0.06494141  0.02539062  0.05615234  0.24609375 -0.20507812\n",
      "  0.23632812 -0.00860596 -0.02294922  0.05078125  0.10644531 -0.03564453\n",
      "  0.08740234 -0.05712891  0.08496094  0.23535156 -0.10107422 -0.03564453\n",
      " -0.04736328  0.04736328 -0.14550781 -0.10986328  0.14746094 -0.23242188\n",
      " -0.07275391  0.19628906 -0.37890625 -0.07226562  0.04833984  0.11914062\n",
      "  0.06103516 -0.12109375 -0.27929688  0.05200195  0.04907227 -0.02709961\n",
      "  0.1328125   0.03369141 -0.32226562  0.04223633 -0.08789062  0.15429688\n",
      "  0.09472656  0.10351562 -0.02856445  0.00128174 -0.00427246  0.24609375\n",
      " -0.05957031 -0.16894531 -0.09619141  0.16796875  0.0133667   0.04882812\n",
      "  0.08349609  0.06347656 -0.00872803 -0.08642578 -0.03857422 -0.08251953\n",
      "  0.15722656  0.22753906 -0.00762939 -0.19921875 -0.06347656  0.12792969\n",
      " -0.06347656 -0.03027344  0.0456543   0.06298828 -0.02526855 -0.06787109\n",
      " -0.01141357 -0.13574219  0.02978516  0.10400391 -0.15917969 -0.08447266\n",
      "  0.29882812 -0.12597656  0.11425781 -0.08105469 -0.09082031 -0.07910156\n",
      " -0.11181641 -0.09619141  0.02770996  0.14257812 -0.26757812 -0.09375\n",
      "  0.03979492 -0.17871094 -0.02819824  0.01464844 -0.31640625 -0.24511719\n",
      " -0.08935547  0.09716797 -0.00964355 -0.14746094  0.15234375  0.21582031\n",
      "  0.05981445  0.23828125 -0.05151367  0.14941406  0.13574219 -0.03222656\n",
      " -0.265625   -0.11181641 -0.23046875 -0.140625    0.25585938 -0.15429688\n",
      "  0.1796875   0.15527344 -0.21582031  0.36328125 -0.1015625   0.04980469\n",
      "  0.07177734 -0.14550781 -0.03198242  0.00952148 -0.12109375  0.12109375\n",
      "  0.09765625  0.07763672  0.3203125  -0.22265625 -0.08447266 -0.10742188\n",
      "  0.11279297 -0.13867188 -0.21875     0.0145874   0.13378906 -0.00921631\n",
      "  0.00921631  0.16894531  0.16894531 -0.078125   -0.00665283  0.03735352\n",
      " -0.10888672 -0.25390625  0.01452637 -0.09716797 -0.19628906 -0.01782227\n",
      " -0.28125    -0.02050781 -0.02905273 -0.09375    -0.17675781  0.21484375\n",
      " -0.05224609 -0.11572266 -0.01977539 -0.10839844 -0.01342773 -0.15332031\n",
      " -0.140625   -0.11816406  0.09228516  0.109375    0.05761719 -0.03466797\n",
      "  0.03564453 -0.12011719 -0.14257812 -0.00072479 -0.06689453  0.11914062\n",
      " -0.10449219  0.07861328 -0.12792969  0.09570312 -0.00817871  0.07128906\n",
      "  0.20703125 -0.03149414  0.09570312  0.17285156 -0.07958984 -0.02429199\n",
      " -0.07519531 -0.07568359  0.09521484 -0.06494141 -0.00689697 -0.09033203\n",
      "  0.03100586  0.19921875 -0.10644531 -0.11474609  0.18652344 -0.05078125\n",
      "  0.0859375   0.00128937 -0.18847656 -0.20019531 -0.02832031  0.11328125\n",
      "  0.25976562  0.22070312  0.04101562  0.00171661  0.07568359 -0.01196289\n",
      "  0.0177002  -0.05883789 -0.25976562 -0.234375   -0.04956055  0.25976562\n",
      "  0.15332031  0.15136719  0.08300781 -0.15527344  0.04931641  0.07519531\n",
      " -0.05078125 -0.1328125  -0.13574219  0.04199219 -0.14257812  0.02099609\n",
      "  0.07861328  0.01611328  0.01623535 -0.21582031  0.01599121 -0.04882812\n",
      " -0.02404785  0.13476562  0.08496094 -0.01196289  0.10009766 -0.13867188\n",
      "  0.08056641 -0.22070312 -0.12011719  0.18945312  0.05444336 -0.05053711\n",
      "  0.00147247  0.14160156 -0.06494141 -0.05566406 -0.09033203 -0.0267334\n",
      " -0.10498047  0.02416992  0.01422119  0.1875     -0.16503906  0.01538086\n",
      " -0.04174805  0.05444336 -0.01184082 -0.15625     0.00193024 -0.06982422]\n"
     ]
    }
   ],
   "source": [
    "print(Word2VecModel['good'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf2887b",
   "metadata": {
    "papermill": {
     "duration": 0.07775,
     "end_time": "2021-12-07T14:23:42.972094",
     "exception": false,
     "start_time": "2021-12-07T14:23:42.894344",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In the output of the above code cell, we can see a 300-dimensional real-valued vector for the word \"good\".\n",
    "\n",
    "The above few cells were about using pre-trained word2vec representation now in upcoming cells we will focus on learning/Training our own word2vec representations."
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
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"
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     "end_time": "2021-12-07T14:23:43.126254",
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     "start_time": "2021-12-07T14:23:43.049508",
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   "source": [
    "**Training our own embeddings**\n",
    "\n",
    "Now we’ll focus on training our own word embeddings. For this, we’ll look at two architectural variants that were proposed in the original Word2vec approach. The two variants are:\n",
    "\n",
    "1. Continuous bag of words (CBOW)\n",
    "2. SkipGram\n",
    "\n",
    "Both of these have a lot of similarities in many respects. \n",
    "\n",
    "Throughout this section, we’ll use the sentence “The quick brown fox jumps over the lazy dog” as our example text.\n",
    "\n",
    "**1. Continuous bag of words (CBOW)**\n",
    "\n",
    "In CBOW, the primary task is to build a language model that correctly predicts the center word given the context words in which the center word appears. Consider our example sentence we take the word “jumps” as the center word, then its context is formed by words in its vicinity. If we take the context size of 2, then for our example, the context is given by brown, fox, over, the. CBOW uses the context words to predict the target word—jumps—as shown in the below figure \n",
    "<br><br>\n",
    "\n",
    "![image-1.png](attachment:image.png)\n",
    "\n",
    "<br><br>\n",
    "Now next task is to create a training sample of the form (X, Y) for this task where X will be context words and Y will be Center word. We define the value of context window = 2 in this case. \n",
    "![image-3.png](attachment:image-2.png)\n",
    "\n",
    "<br><br>\n",
    "Now that we have the training data ready, let’s focus on the model. For this, we construct a shallow net (it’s shallow since it has a single hidden layer). We assume we want to learn D-dim word embeddings. Further, let V be the vocabulary of the text corpus"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "dfa4b64d",
   "metadata": {
    "papermill": {
     "duration": 0.078419,
     "end_time": "2021-12-07T14:23:43.280598",
     "exception": false,
     "start_time": "2021-12-07T14:23:43.202179",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![image-4.png](attachment:image.png)\n",
    "\n",
    "\n",
    "<br><br>\n",
    "The objective is to learn an embedding matrix E|V| x d.To begin with, we initialize the matrix randomly. Here, |V| is the size of corpus vocabulary and d is the dimension of the embedding. Let’s break down the shallow net in Figure layer by layer. In the input layer, indices of the words in context are used to fetch the corresponding rows from the embedding matrix E|V| x d. The vectors fetched are then added to get a single D-dim vector, and this is passed to the next layer. The next layer simply takes this d vector and multiplies it with another matrix E’d x |V|.. This gives a 1 x |V| vector, which is fed to a softmax function to get probability distribution over the vocabulary space. This distribution is compared with the label and uses backpropagation to update both the matrices E and E’ accordingly. At the end of the training, E is the embedding matrix we wanted to learn.\n",
    "<br><br>"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "41c5ab1b",
   "metadata": {
    "papermill": {
     "duration": 0.086327,
     "end_time": "2021-12-07T14:23:43.464620",
     "exception": false,
     "start_time": "2021-12-07T14:23:43.378293",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**2. SkipGram** \n",
    "\n",
    "SkipGram is very similar to CBOW, with some minor changes. In Skip‐ Gram, the task is to predict the context words from the center word. For our toy corpus with context size 2, using the center word “jumps,” we try to predict every word in context—“brown,” “fox,” “over,” “the”—as shown in the Figure below \n",
    "\n",
    "![image-5.png](attachment:image.png)\n",
    "\n",
    "Now we will create a training sample of the form (X, Y) for this task where X will be the center word and Y will be Context words. \n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "![image-6.png](attachment:image-2.png)\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "![image-9.png](attachment:image-3.png)\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "The shallow network used to train the SkipGram model, shown in the below Figure, is very similar to the network used for CBOW, with some minor changes. In the input layer, the index of the word in the target is used to fetch the corresponding row from the embedding matrix E|V| x d. The vectors fetched are then passed to the next layer. The next layer simply takes this d vector and multiplies it with another matrix E’d x |V|. This gives a 1 x |V| vector, which is fed to a softmax function to get probability distribution over the vocabulary space. This distribution is compared with the label and uses backpropagation to update both the matrices E and E’ accordingly. At the end of the training, E is the embedding matrix we wanted to learn."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0696ba4c",
   "metadata": {
    "papermill": {
     "duration": 0.08588,
     "end_time": "2021-12-07T14:23:43.634447",
     "exception": false,
     "start_time": "2021-12-07T14:23:43.548567",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Implementation** : \n",
    "\n",
    "One of the most commonly used implementations is with gensim. We have to choose several hyperparameters (i.e., the variables that need to be set before starting the training process). Let’s look at two examples.\n",
    "\n",
    "Dimensionality of the word vectors\n",
    "\n",
    "As the name indicates, this decides the space of the learned embeddings. While there is no ideal number, it’s common to construct word vectors with dimensions in the range of 50–500 and evaluate them on the task we’re using them for to choose the best option. In gensim we do this by setting the \"vector_size\" parameter to the size we want. \n",
    "\n",
    "Context window\n",
    "\n",
    "How long or short the context we look for to learn the vector representation is. In gensim we do this by setting the \"window\" parameter to the size we want.\n",
    "\n",
    "There are also other choices we make, such as whether to use CBOW or SkipGram to\n",
    "learn the embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b536590",
   "metadata": {
    "papermill": {
     "duration": 0.077982,
     "end_time": "2021-12-07T14:23:43.789609",
     "exception": false,
     "start_time": "2021-12-07T14:23:43.711627",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next few code cells shows implementation of CBOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "35977b6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:23:43.953853Z",
     "iopub.status.busy": "2021-12-07T14:23:43.953061Z",
     "iopub.status.idle": "2021-12-07T14:23:44.203439Z",
     "shell.execute_reply": "2021-12-07T14:23:44.203922Z",
     "shell.execute_reply.started": "2021-12-07T13:18:42.114493Z"
    },
    "papermill": {
     "duration": 0.335098,
     "end_time": "2021-12-07T14:23:44.204144",
     "exception": false,
     "start_time": "2021-12-07T14:23:43.869046",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentences on Which We are gonna train our CBOW Word2Vec Model:\n",
      "\n",
      "[['human', 'interface', 'computer'], ['survey', 'user', 'computer', 'system', 'response', 'time'], ['eps', 'user', 'interface', 'system'], ['system', 'human', 'system', 'eps'], ['user', 'response', 'time'], ['trees'], ['graph', 'trees'], ['graph', 'minors', 'trees'], ['graph', 'minors', 'survey']]\n",
      "Model Saved\n"
     ]
    }
   ],
   "source": [
    "from gensim.test.utils import common_texts\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "print(\"Sentences on Which We are gonna train our CBOW Word2Vec Model:\\n\")\n",
    "print(common_texts)\n",
    "\n",
    "Our_CBOW_Word2Vec_Model = Word2Vec(common_texts, vector_size = 10, window = 5, min_count = 1, workers = 8, sg = 0)\n",
    "Our_CBOW_Word2Vec_Model.save(\"Our_CBOW_Word2Vec_Model.w2v\")\n",
    "print(\"Model Saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8ce08a4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:23:44.365895Z",
     "iopub.status.busy": "2021-12-07T14:23:44.365205Z",
     "iopub.status.idle": "2021-12-07T14:23:44.367650Z",
     "shell.execute_reply": "2021-12-07T14:23:44.368153Z",
     "shell.execute_reply.started": "2021-12-07T13:18:42.303881Z"
    },
    "papermill": {
     "duration": 0.087106,
     "end_time": "2021-12-07T14:23:44.368324",
     "exception": false,
     "start_time": "2021-12-07T14:23:44.281218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('graph', 0.3586883544921875),\n",
       " ('system', 0.22743134200572968),\n",
       " ('time', 0.1153423935174942),\n",
       " ('interface', 0.09816539287567139),\n",
       " ('survey', 0.014487894251942635)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Our_CBOW_Word2Vec_Model.wv.most_similar('human', topn = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "06cb89bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:23:44.525861Z",
     "iopub.status.busy": "2021-12-07T14:23:44.525200Z",
     "iopub.status.idle": "2021-12-07T14:23:44.530372Z",
     "shell.execute_reply": "2021-12-07T14:23:44.530968Z",
     "shell.execute_reply.started": "2021-12-07T13:18:42.314072Z"
    },
    "papermill": {
     "duration": 0.085874,
     "end_time": "2021-12-07T14:23:44.531142",
     "exception": false,
     "start_time": "2021-12-07T14:23:44.445268",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00410223, -0.0836895 , -0.05600014,  0.07104538,  0.0335254 ,\n",
       "        0.07225668,  0.06800246,  0.07530741, -0.03789156, -0.00561807],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Our_CBOW_Word2Vec_Model.wv['human']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f769e21b",
   "metadata": {
    "papermill": {
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     "end_time": "2021-12-07T14:23:44.694148",
     "exception": false,
     "start_time": "2021-12-07T14:23:44.609882",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "The Above cells show code implementation of the CBOW model one can experiment with tunning hyperparameters. Interested one can experiment by setting sg hyperparameter to 1 then the model will become a skip-gram model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf09d92",
   "metadata": {
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   },
   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Glove Word Embeddings</h1><a id = \"9\" ></a>\n",
    "\n",
    "GloVe Stands for Global Vectors for word representation is another word embedding technique that was developed as an open-source project at Stanford and was launched in 2014. Just to refresh, word vectors put words to a nice vector space, where similar words cluster together and different words repel. The advantage of GloVe is that, unlike Word2vec, GloVe does not rely just on local statistics (local context information of words), but incorporates global statistics (word co-occurrence) to obtain word vectors. But keep in mind that there’s quite a bit of synergy between the GloVe and Word2vec. The gloVe can be used to find relations between words like synonyms, company-product relations, zip codes, and cities, etc.\n",
    "\n",
    "The question may arise Why do we need Glove if we have word2vec as a good word embedding technique Because Word2vec relies only on local information of language. That is, the semantics learned for a given word, are only affected by the surrounding words.\n",
    "\n",
    "For example, take the sentence,\n",
    "\n",
    "The cat sat on the mat\n",
    "\n",
    "If you use Word2vec, it wouldn’t capture information like,\n",
    "\n",
    "is “the” a special context of the words “cat” and “mat” ?\n",
    "\n",
    "or\n",
    "\n",
    "is “the” just a stopword?\n",
    "\n",
    "This can be suboptimal, especially in the eye of theoreticians.\n",
    "\n",
    "GloVe method is built on an important idea, You can derive semantic relationships between words from the co-occurrence matrix. Given a corpus having V words, the co-occurrence matrix X will be a V x V matrix, where the i th row and j th column of X, X_ij denotes how many times word i has co-occurred with word j. An example co-occurrence matrix might look as follows.\n",
    "\n",
    "![](https://miro.medium.com/max/434/1*QWcK8CIDs8kMkOwsOxvywA.png)\n",
    "\n",
    "The co-occurrence matrix for the sentence “the cat sat on the mat” with a window size of 1. As you probably noticed it is a symmetric matrix.\n",
    "\n",
    "For detailed knowledge about Glove word embedding, you can refer [This article](https://towardsdatascience.com/light-on-math-ml-intuitive-guide-to-understanding-glove-embeddings-b13b4f19c010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cb57ec6c",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: DeprecationWarning: Call to deprecated `glove2word2vec` (KeyedVectors.load_word2vec_format(.., binary=False, no_header=True) loads GLoVE text vectors.).\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(400000, 100)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Glove_path = \"../input/glove6b/glove.6B.100d.txt\"\n",
    "from gensim.scripts.glove2word2vec import glove2word2vec\n",
    "word2vec_output_file = 'glove.6B.100d.txt.word2vec'\n",
    "glove2word2vec(Glove_path, word2vec_output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "52b5c0c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:25:35.087332Z",
     "iopub.status.busy": "2021-12-07T14:25:35.086663Z",
     "iopub.status.idle": "2021-12-07T14:26:49.609550Z",
     "shell.execute_reply": "2021-12-07T14:26:49.608924Z",
     "shell.execute_reply.started": "2021-12-07T13:20:30.751097Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "# load the Stanford GloVe model\n",
    "filename = './glove.6B.100d.txt.word2vec'\n",
    "Glove_model = KeyedVectors.load_word2vec_format(filename, binary=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "38a95065",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:26:49.772334Z",
     "iopub.status.busy": "2021-12-07T14:26:49.771637Z",
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     "shell.execute_reply.started": "2021-12-07T13:21:45.496829Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most similar words to word 'human' : \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('animal', 0.7462460398674011),\n",
       " ('rights', 0.7322573661804199),\n",
       " ('humans', 0.6607711911201477),\n",
       " ('animals', 0.6567643284797668),\n",
       " ('body', 0.6552972197532654),\n",
       " ('nature', 0.6483666896820068),\n",
       " ('beings', 0.6467736959457397),\n",
       " ('organization', 0.6319881081581116),\n",
       " ('scientific', 0.630841076374054),\n",
       " ('common', 0.6211239099502563)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Most similar words to word 'human' : \")\n",
    "Glove_model.most_similar('human')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e85d69b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:26:50.127328Z",
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     "shell.execute_reply": "2021-12-07T14:26:50.131512Z",
     "shell.execute_reply.started": "2021-12-07T13:21:45.615577Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glove Word Embeddings of word 'human' \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 3.3864e-01,  5.9663e-01,  5.3322e-01,  3.1404e-01,  1.5321e-01,\n",
       "        3.1749e-01, -4.2940e-01, -2.9150e-01, -2.1047e-03, -3.9309e-01,\n",
       "       -8.5441e-01, -8.0708e-02,  1.2118e+00,  6.9316e-02,  8.0613e-03,\n",
       "        8.7888e-01,  3.1908e-02,  5.8655e-01, -5.4892e-01, -7.8468e-03,\n",
       "        1.7327e-01, -2.6693e-01,  4.2802e-01,  6.6123e-02,  5.1847e-01,\n",
       "        7.7226e-01,  2.0608e-01, -4.5836e-01,  3.5485e-01,  7.1547e-01,\n",
       "        6.0855e-01,  2.0254e-01, -4.8756e-01,  5.7974e-01,  8.6728e-02,\n",
       "       -5.1852e-01, -3.7274e-01,  1.0014e+00, -2.9259e-01,  3.2290e-01,\n",
       "       -9.7563e-01, -2.2288e-01, -2.3335e-01, -2.6891e-01,  1.4612e-01,\n",
       "        1.2004e-01, -2.0402e-01, -9.4647e-02, -1.5402e+00, -5.9510e-02,\n",
       "        1.0887e+00, -2.4998e-01, -2.5808e-01,  1.2798e+00, -1.2849e-01,\n",
       "       -1.4511e+00, -2.4686e-01, -9.5046e-02,  1.7425e+00,  1.1977e-01,\n",
       "       -1.9206e-01,  4.4368e-01, -1.6453e-01, -7.6663e-01,  1.1100e+00,\n",
       "        4.6748e-01, -2.4673e-02,  4.7179e-03,  6.9761e-01, -2.2975e-01,\n",
       "        6.4385e-01, -8.9847e-02,  7.8711e-02, -4.1255e-02,  5.3239e-01,\n",
       "       -3.9945e-01, -4.6565e-01, -2.7601e-01, -1.5407e+00,  5.3639e-01,\n",
       "        5.0511e-01,  9.7199e-01,  1.2057e-01,  6.1720e-01, -2.1424e+00,\n",
       "        6.6596e-01,  3.7060e-01,  2.6115e-01, -1.3408e-01, -3.9319e-01,\n",
       "        6.3069e-01, -5.4047e-01,  1.5467e-01,  4.0616e-01,  1.0424e-01,\n",
       "       -4.7552e-02, -8.1695e-01, -1.1976e+00,  6.9475e-01, -1.9884e-01],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Glove Word Embeddings of word 'human' \")\n",
    "Glove_model['human']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0760834a",
   "metadata": {
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     "status": "completed"
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   },
   "source": [
    "**Advantages of Word2Vec:**\n",
    "\n",
    "1. The idea is very intuitive, which transforms the unlabeled raw corpus into labeled data (by mapping the target word to its context word), and learns the representation of words in a classification task.\n",
    "2. The data can be fed into the model in an online way and needs little preprocessing, thus requiring little memory.\n",
    "3. the mapping between the target word to its context word implicitly embeds the sub-linear relationship into the vector space of words, so that relationships like “king:man as queen: woman” can be inferred by word vectors.\n",
    "3. It is simple for a freshman to understand the principle and do the implementation.\n",
    "\n",
    "**Disadvantages of Word2Vec:**\n",
    "\n",
    "1. The sub-linear relationships are not explicitly defined. There is little theoretical support behind such characteristics.\n",
    "2. The model could be very difficult to train if use the softmax function, since the number of categories is too large (the size of vocabulary). Though approximation algorithms like negative sampling (NEG) and hierarchical softmax (HS) are proposed to address the issue, other problems happen. For example, the word vectors by NEG are not distributed uniformly, they are located within a cone in the vector space hence the vector space is not sufficiently utilized.\n",
    "3. It still provides no way to address the **out of vocabulary(OOV)** problem.\n",
    "\n",
    "**Advantages of Glove:**\n",
    "\n",
    "1. The goal of Glove is very straightforward, i.e., to enforce the word vectors to capture sub-linear relationships in the vector space. Thus, it proves to perform better than Word2vec in the word analogy tasks.\n",
    "2. Glove adds some more practical meaning to word vectors by considering the relationships between word pair and word pair rather than word and word.\n",
    "3. Glove gives lower weight for highly frequent word pairs to prevent the meaningless stop words like “the”, “an” will not dominating the training progress.\n",
    "\n",
    "**Disadvantages of Glove:**\n",
    "\n",
    "1. The model is trained on the co-occurrence matrix of words, which takes a lot of memory for storage. Especially, if you change the hyper-parameters related to the co-occurrence matrix, you have to reconstruct the matrix again, which is very time-consuming.\n",
    "\n",
    "**Both Word2vec and Glove do not solve the problems like:**\n",
    "\n",
    "1. How to learn the representation for out-of-vocabulary words.\n",
    "2. How to separate some opposite word pairs. For example, “good” and “bad” are usually located very close to each other in the vector space, which may limit the performance of word vectors in NLP tasks like sentiment analysis.\n",
    "\n",
    "Content of this cell is taken from [Quora](https://www.quora.com/What-are-the-advantages-and-disadvantages-of-Word2vec-and-GloVe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cf12a0",
   "metadata": {
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     "start_time": "2021-12-07T14:26:50.371715",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "Despite the ease of using Powerfull Word Embeddings like Word2vec or any such word embedding to do feature extraction from texts, **we don’t have a good way of handling OOV words yet**.\n",
    "\n",
    "What We can do to solve this Problem?\n",
    "\n",
    "1. A simple approach that often works is to exclude those words from the feature extraction process so we don’t have to worry about how to get their representations.\n",
    "\n",
    "2. Another way to deal with the OOV problem for word embeddings is to create vectors that are initialized randomly.\n",
    "\n",
    "3. There are also other approaches that handle the OOV problem by modifying the training process by bringing in characters and other subword-level linguistic components. Let’s look at one such approach now. The key idea is that one can potentially handle the OOV problem by using subword information, such as morphological properties (e.g., prefixes, suffixes, word endings, etc.), **or by using character representations. fastText, from Facebook AI research**, is one of the popular algorithms that follows this approach.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8224cc0",
   "metadata": {
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   },
   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">FastText Word Embeddings</h1><a id = \"10\" ></a>\n",
    "\n",
    "A word can be represented by its constituent character ngrams. Following a similar architecture to Word2vec, fastText learns embeddings for words and character n-grams together and views a word’s embedding vector as an aggregation of its constituent character n-grams. This makes it possible to generate embeddings even for words that are not present in the vocabulary. Say there’s a word, “gregarious,” that’s not found in the embedding’s word vocabulary. We break it into character n-grams—gre, reg, ega, ….ous—and combine these embeddings of the ngrams to arrive at the embedding of “gregarious.”\n",
    "\n",
    "How FastText Works?\n",
    "\n",
    "FastText is a modified version of word2vec (i.e.. Skip-Gram and CBOW). The only difference between fastText vs word2vec is its pooling strategies (what are the input, output, and dictionary of the model). In word2vec each word is represented as a bag of words but in FastText each word is represented as a bag of character n-gram.\n",
    "\n",
    "**character n-grams** the contiguous sequence of n items from a given sample of a character or word. It may be bigram, trigram, etc.\n",
    "For example character trigram (n = 3) of the word “where” will be:\n",
    "\n",
    "<wh, whe, her, ere, re>\n",
    "\n",
    "In FastText architecture, they have also included the word itself with the character n-gram. That means input data to the model for the word “eating” will be:\n",
    "\n",
    "![](https://amitness.com/images/fasttext-center-word-embedding.png)\n",
    "\n",
    "Now the model I am referring same is word2vec which is a shallow neural network with one hidden layer as discussed above.\n",
    "\n",
    "\n",
    "Now to prepare training data for the (Skip-Gram-based) FastText model, we define “context word” as the word which follows a given word in the text (which will be our “target word”). That means we will be predicting the surrounding word for a given word.\n",
    "\n",
    "Note: FastText word embeddings support both Continuous Bag of Words (CBOW) and Skip-Gram models. I will explain and implement the skip-gram model in the below cell to learn vector representation (FastText word embeddings). Now let’s construct our training examples (like Skip-Gram), scanning through the text with a window will prepare a context word and a target word.\n",
    "\n",
    "Consider the sentence : \n",
    "\n",
    "<div style = \"text-align:center\"><b> i like natural language processing</b></div>\n",
    "\n",
    "![](https://secureservercdn.net/45.40.148.234/um0.ec8.myftpupload.com/wp-content/uploads/2020/10/Picture2.png)\n",
    "\n",
    "For the above example, for context words “i” and “natural” the target word will be “like”. Full training data for FastText word embedding will look like below. By observing the below training data, your confusion of fastText vs word2vec should be clear.\n",
    "\n",
    "\n",
    "Now you know in word2vec (skip-gram) each word is represented as a bag of words but in FastText each word is represented as a bag of character n-gram. This training data preparation is the only difference between FastText word embeddings and skip-gram (or CBOW) word embeddings.\n",
    "\n",
    "After training data preparation of FastText, training the word embedding, finding word similarity, etc. are the same as the word2vec model (for our example similar to the skip-gram model).\n",
    "\n",
    "Now let’s see how to implement FastText word embeddings in python using Gensim library.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1e1cfdf0",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most similar words to word 'human' : \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('non-human', 0.7691742181777954),\n",
       " ('Human', 0.7620595693588257),\n",
       " ('nonhuman', 0.7084148526191711),\n",
       " ('beings', 0.7024695873260498),\n",
       " ('humans', 0.6974276304244995),\n",
       " ('animal', 0.6924618482589722),\n",
       " ('humanity', 0.6476197838783264),\n",
       " ('human-', 0.6355127692222595),\n",
       " ('mammalian', 0.6191367506980896),\n",
       " ('natural', 0.6171244978904724)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models.fasttext import load_facebook_model\n",
    "from gensim.models import FastText, KeyedVectors\n",
    "\n",
    "fasttext_model = KeyedVectors.load_word2vec_format('../input/fasttext-wikinews/wiki-news-300d-1M.vec')\n",
    "\n",
    "# fasttext_model = FastText.load_fasttext_format('../input/fasttext-crawl-300d-2m/crawl-300d-2M.vec')\n",
    "print(\"Most similar words to word 'human' : \")\n",
    "fasttext_model.most_similar('human')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1bc78fd1",
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     "end_time": "2021-12-07T14:35:55.937752",
     "exception": false,
     "start_time": "2021-12-07T14:35:55.732987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glove Word Embeddings of word 'human' \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 8.800e-03, -1.230e-02,  3.650e-02,  1.136e-01, -8.000e-03,\n",
       "        1.574e-01,  9.080e-02,  1.293e-01, -1.400e-03,  1.081e-01,\n",
       "       -8.610e-02, -3.450e-02, -5.610e-02,  3.300e-03, -4.000e-04,\n",
       "        1.650e-02,  8.540e-02,  4.670e-02, -1.632e-01,  6.200e-02,\n",
       "        5.800e-03,  1.162e-01, -7.390e-02,  1.525e-01, -6.330e-02,\n",
       "        6.780e-02, -1.114e-01, -3.440e-02,  4.310e-02,  6.050e-02,\n",
       "       -1.349e-01,  5.660e-02, -7.210e-02,  1.785e-01,  5.520e-02,\n",
       "       -8.580e-02, -7.610e-02,  1.387e-01, -3.760e-02,  8.000e-03,\n",
       "        1.093e-01, -6.550e-02,  7.130e-02, -1.020e-01, -6.250e-02,\n",
       "       -6.340e-02, -6.770e-02, -8.940e-02,  5.400e-03,  6.740e-02,\n",
       "       -8.720e-02,  1.085e-01, -7.148e-01,  5.700e-03, -4.660e-02,\n",
       "        3.980e-02, -7.630e-02,  9.780e-02, -3.300e-03,  1.379e-01,\n",
       "       -1.192e-01,  3.700e-03, -1.599e-01, -9.340e-02, -1.021e-01,\n",
       "       -2.812e-01,  1.539e-01, -2.750e-02, -3.230e-02, -3.860e-02,\n",
       "       -1.403e-01, -7.450e-02, -7.510e-02,  1.232e-01,  8.800e-03,\n",
       "       -9.230e-02, -2.520e-02,  6.200e-02,  3.040e-02,  3.030e-01,\n",
       "       -1.490e-02,  4.990e-02,  1.314e-01, -1.909e-01,  8.500e-03,\n",
       "       -7.300e-03,  7.720e-02, -1.769e-01,  3.504e-01, -7.650e-02,\n",
       "        4.000e-02, -6.060e-02,  9.160e-02, -2.170e-02,  1.309e-01,\n",
       "        1.270e-02,  8.000e-04, -1.289e-01,  3.690e-02,  2.852e-01,\n",
       "       -1.613e-01, -4.230e-02,  5.840e-02, -1.020e-02,  1.890e-02,\n",
       "        2.270e-02,  1.939e-01,  2.150e-02, -7.990e-02, -5.360e-02,\n",
       "       -1.776e-01,  1.737e-01, -1.750e-02, -6.500e-02, -3.280e-02,\n",
       "        5.130e-02,  4.980e-02,  7.300e-03, -3.970e-02, -2.032e-01,\n",
       "        5.160e-02, -4.910e-02, -1.086e-01,  7.510e-02,  5.140e-02,\n",
       "        2.141e-01, -1.320e-02,  3.650e-02,  2.760e-02,  1.104e-01,\n",
       "       -1.000e-02, -9.150e-02,  1.560e-01, -3.750e-02, -1.490e-02,\n",
       "       -1.823e-01, -7.740e-02,  1.927e-01,  6.080e-02, -2.231e-01,\n",
       "        7.050e-02,  2.000e-02, -4.080e-02,  2.354e-01,  8.400e-03,\n",
       "       -4.500e-03,  5.060e-02, -1.730e-02,  1.409e-01, -9.430e-02,\n",
       "       -7.950e-02, -1.155e-01, -4.830e-02,  3.570e-02,  6.130e-02,\n",
       "        9.910e-02,  1.160e-02, -2.600e-03,  7.490e-02,  9.700e-03,\n",
       "       -9.020e-02,  1.664e-01, -4.080e-02, -5.850e-02,  2.000e-03,\n",
       "        9.180e-02,  1.312e-01,  5.070e-02, -3.300e-02,  8.990e-02,\n",
       "       -7.250e-02, -8.290e-02, -1.383e-01,  3.240e-02,  6.940e-02,\n",
       "       -1.532e-01,  3.353e-01, -2.800e-02,  1.170e-02, -3.120e-02,\n",
       "        6.600e-03,  1.590e-02,  6.450e-02, -1.630e-02,  6.240e-02,\n",
       "        4.270e-02,  7.520e-02,  2.870e-02, -6.200e-02,  1.186e-01,\n",
       "        1.551e-01, -1.339e-01,  7.610e-02, -1.146e-01,  7.200e-03,\n",
       "       -9.400e-03, -6.230e-02,  1.084e-01,  1.366e-01, -5.000e-03,\n",
       "       -2.360e-02,  1.105e-01, -9.430e-02, -2.500e-02, -2.270e-02,\n",
       "        6.700e-03,  4.170e-02, -1.663e-01, -6.110e-02,  2.230e-01,\n",
       "        6.910e-02, -7.020e-02,  4.420e-02, -7.630e-02, -3.030e-02,\n",
       "        1.221e-01,  6.430e-02,  2.770e-02, -9.480e-02, -1.500e-01,\n",
       "        1.330e-02, -5.110e-02, -1.250e-02,  1.145e-01, -1.244e-01,\n",
       "       -8.300e-03,  6.200e-03,  2.038e-01,  1.102e-01,  5.660e-02,\n",
       "        2.725e-01,  1.710e-02,  2.873e-01, -1.072e-01,  1.991e-01,\n",
       "       -2.468e-01, -2.590e-02, -1.153e-01, -2.346e-01,  2.000e-04,\n",
       "       -4.000e-02,  1.813e-01,  7.000e-03,  2.070e-02,  1.600e-02,\n",
       "        5.570e-02, -1.457e-01,  3.390e-02, -1.659e-01,  4.385e-01,\n",
       "        5.600e-03,  2.490e-02, -7.440e-02, -2.670e-02,  3.310e-02,\n",
       "        1.816e-01, -3.240e-02,  2.031e-01,  1.900e-02, -2.710e-02,\n",
       "       -7.970e-02, -1.566e-01,  5.000e-02, -5.820e-02, -1.322e-01,\n",
       "       -1.390e-02, -1.447e-01,  6.340e-02, -2.163e-01, -4.060e-02,\n",
       "       -3.030e-02,  6.150e-02, -1.040e-02, -1.490e-02, -1.028e-01,\n",
       "       -9.330e-02,  1.724e-01, -3.100e-03,  7.770e-02,  1.028e-01,\n",
       "        7.350e-02, -6.870e-02,  3.490e-02,  5.800e-02,  7.360e-02,\n",
       "       -3.260e-02,  1.617e-01, -3.530e-02,  1.840e-02,  1.640e-02,\n",
       "       -3.350e-02, -1.355e-01,  7.200e-02,  7.280e-02,  1.710e-02,\n",
       "       -4.510e-02,  5.940e-02,  8.270e-02, -6.300e-02, -1.246e-01],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Glove Word Embeddings of word 'human' \")\n",
    "fasttext_model['human']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bbc81fd",
   "metadata": {
    "papermill": {
     "duration": 0.081699,
     "end_time": "2021-12-07T14:35:56.103060",
     "exception": false,
     "start_time": "2021-12-07T14:35:56.021361",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Training our own fasttext model using python's gensim library by settings up below listed hyperparameters:\n",
    "\n",
    "\n",
    "- size: Dimensionality of the word vectors. window=window_size,\n",
    "- min_count: The model ignores all words with total frequency lower than this.\n",
    "- sample: The threshold for configuring which higher-frequency words are randomly down sampled, useful range is (0, 1e-5).\n",
    "- workers: Use these many worker threads to train the model (=faster training with multicore machines).\n",
    "- sg: Training algorithm: skip-gram if sg=1, otherwise CBOW.\n",
    "- iter: Number of iterations (epochs) over the corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5a2b6ef3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:35:56.273815Z",
     "iopub.status.busy": "2021-12-07T14:35:56.273143Z",
     "iopub.status.idle": "2021-12-07T14:35:57.503746Z",
     "shell.execute_reply": "2021-12-07T14:35:57.504320Z",
     "shell.execute_reply.started": "2021-12-07T13:39:58.044382Z"
    },
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     "end_time": "2021-12-07T14:35:57.504499",
     "exception": false,
     "start_time": "2021-12-07T14:35:56.186706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from gensim.models import FastText\n",
    "from gensim.test.utils import common_texts\n",
    "\n",
    "our_fasttext_model = FastText(common_texts, vector_size = 100, min_count = 1, window = 5, sg = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "735b4230",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:35:57.671617Z",
     "iopub.status.busy": "2021-12-07T14:35:57.670975Z",
     "iopub.status.idle": "2021-12-07T14:35:57.678658Z",
     "shell.execute_reply": "2021-12-07T14:35:57.679209Z",
     "shell.execute_reply.started": "2021-12-07T13:39:59.411713Z"
    },
    "papermill": {
     "duration": 0.093522,
     "end_time": "2021-12-07T14:35:57.679377",
     "exception": false,
     "start_time": "2021-12-07T14:35:57.585855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Similar words of word 'computer' : \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('user', 0.15659411251544952),\n",
       " ('response', 0.1238383799791336),\n",
       " ('eps', 0.030705023556947708),\n",
       " ('system', 0.02557401731610298),\n",
       " ('interface', 0.005858910735696554),\n",
       " ('survey', -0.03156965970993042),\n",
       " ('minors', -0.05455636978149414),\n",
       " ('human', -0.06685879826545715),\n",
       " ('time', -0.06855909526348114),\n",
       " ('trees', -0.10636060684919357)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Most Similar words of word 'computer' : \")\n",
    "our_fasttext_model.wv.most_similar('computer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "84575cff",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2021-12-07T14:35:57.853160Z",
     "shell.execute_reply": "2021-12-07T14:35:57.852529Z",
     "shell.execute_reply.started": "2021-12-07T13:39:59.422795Z"
    },
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     "start_time": "2021-12-07T14:35:57.760637",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.9693521e-04,  3.3105977e-04, -8.7776897e-04,  3.3944362e-04,\n",
       "       -5.0174823e-04, -2.0421455e-03, -1.2406678e-03, -1.9404462e-03,\n",
       "        1.3451086e-03, -2.4126847e-03,  9.1850484e-04, -1.0315110e-03,\n",
       "       -7.6341064e-04,  7.3121635e-05,  1.3828657e-03,  5.1943498e-04,\n",
       "       -2.9885024e-04, -1.1946483e-03, -1.1723848e-03, -6.0895225e-04,\n",
       "       -6.7833957e-04,  3.9277915e-04,  9.8824501e-05,  8.1268896e-04,\n",
       "        5.8197067e-04,  7.0195273e-04, -7.3680707e-04, -1.0396258e-03,\n",
       "       -6.2525918e-04, -2.4049741e-04, -1.1931641e-03, -2.6594134e-04,\n",
       "        7.3604583e-04, -7.2150613e-04, -1.2750807e-03,  1.2423132e-04,\n",
       "        3.7758294e-04, -1.3315529e-03, -2.7344138e-03, -3.0483067e-04,\n",
       "        9.2827156e-04, -7.2816946e-04, -1.1291959e-03, -3.2193188e-04,\n",
       "       -2.0601712e-04, -1.0485471e-04, -6.2297657e-04, -1.6137769e-03,\n",
       "        9.9110638e-04,  9.2298054e-05,  3.6799960e-04, -5.3784001e-04,\n",
       "        1.1332291e-03,  8.7074988e-04, -1.6386734e-03, -8.5585617e-04,\n",
       "       -6.3106982e-04,  6.2290911e-04,  8.4016664e-04, -1.1283027e-03,\n",
       "        1.2916379e-03, -3.4048953e-04, -1.1783368e-03, -1.6085058e-03,\n",
       "        1.5269308e-03,  3.0116282e-05, -2.4188879e-05, -7.2759663e-04,\n",
       "        1.7333046e-03,  8.9342112e-04,  3.2678925e-04, -4.6337990e-04,\n",
       "       -2.3135850e-03, -1.7200562e-03,  4.3593679e-04, -4.1222578e-04,\n",
       "       -1.0668917e-03, -1.0090243e-03, -1.6434486e-03, -1.0506640e-04,\n",
       "        1.0191359e-03, -6.2437344e-04, -1.0817453e-03,  8.8560249e-04,\n",
       "       -1.4572969e-03,  6.4839114e-04,  4.4141297e-04, -1.2450072e-03,\n",
       "        3.4924460e-04, -9.8105113e-04, -9.7378524e-04, -1.9815133e-04,\n",
       "       -1.8950927e-04, -9.8531216e-04,  5.7443278e-04,  1.9900093e-03,\n",
       "        7.1994866e-05,  9.9570793e-04, -1.7088592e-03,  1.3490169e-03],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Word Embedding for Word \"Computer\"\n",
    "\n",
    "our_fasttext_model.wv['computer']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa297125",
   "metadata": {
    "papermill": {
     "duration": 0.083782,
     "end_time": "2021-12-07T14:35:58.019416",
     "exception": false,
     "start_time": "2021-12-07T14:35:57.935634",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can observe results obtained are not very good because I didn't tune the hyperparameters nicely, you can do this task and if you got good results then let me know as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45da4ad8",
   "metadata": {
    "papermill": {
     "duration": 0.08318,
     "end_time": "2021-12-07T14:35:58.185552",
     "exception": false,
     "start_time": "2021-12-07T14:35:58.102372",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A potential problem with both approaches is that they do not take the context of words into account. Take, for example, the sentences “cat sat on mat” and “mat sat on cat.” Both receive the same representation in these approaches, but they obviously have very different meanings. Let’s look at another approach, **Doc2vec**, which allows us to directly learn the representations for texts of arbitrary lengths (phrases, sentences, paragraphs, and documents) by taking the context of words in the text into account.\n",
    "\n",
    "You can Explore Doc2Vec in great detail and I will also try to bring its details in future versions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8204768f",
   "metadata": {
    "papermill": {
     "duration": 0.083164,
     "end_time": "2021-12-07T14:35:58.353154",
     "exception": false,
     "start_time": "2021-12-07T14:35:58.269990",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Important Points Regarding Word Embeddings**\n",
    "\n",
    "1. All text representations are **inherently biased** based on what they saw in training data. For example, an embedding model trained heavily on technology news or articles is likely to identify Apple as being closer to, say, Microsoft or Facebook than to an orange or pear.\n",
    "\n",
    "2. Unlike the basic vectorization approaches, pre-trained embeddings are generally **large-sized files (several gigabytes)**, which may pose problems in certain deployment scenarios. This is something we need to address while using them, otherwise, it can become an engineering bottleneck in performance. The Word2vec model takes ~4.5 GB RAM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea80b0f",
   "metadata": {
    "papermill": {
     "duration": 0.081616,
     "end_time": "2021-12-07T14:35:58.517712",
     "exception": false,
     "start_time": "2021-12-07T14:35:58.436096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h1  style=\"text-align: center\" class=\"list-group-item list-group-item-action active\">Visualizing Embeddings</h1><a id = \"11\" ></a>\n",
    "\n",
    "\n",
    "So far, we’ve seen various vectorization techniques for representing text. The vectors obtained are used as features for the NLP task at hand. An important aspect of any ML project is feature exploration. Visual exploration is a very important aspect of any data-related problem. Even though embeddings are low-dimensional vectors, even\n",
    "100 or 300 dimensions are too high to visualize.\n",
    "\n",
    "t-SNE, or t-distributed Stochastic Neighboring Embedding help us solve this problem. It’s a technique used for visualizing high-dimensional data like embeddings by reducing them to two or three-dimensional data. The technique takes in the embeddings (or any data) and looks at how to best represent the input data using lesser dimensions, all while maintaining the same data distributions in original high-dimensional input space and low-dimensional output space. This, therefore, enables us to plot and visualize the input data. It helps to get a feel for the space of word embedding.\n",
    "\n",
    "Now lets plot scatter plots of most-similar words in the vocabulary of different embedding schemes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2d86f37c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:35:58.694569Z",
     "iopub.status.busy": "2021-12-07T14:35:58.693780Z",
     "iopub.status.idle": "2021-12-07T14:36:03.015835Z",
     "shell.execute_reply": "2021-12-07T14:36:03.014735Z",
     "shell.execute_reply.started": "2021-12-07T14:11:02.370344Z"
    },
    "papermill": {
     "duration": 4.411097,
     "end_time": "2021-12-07T14:36:03.016022",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Visualizing Word2vec Word Embedding \n",
    "\n",
    "keys = ['India', 'good', 'friday', 'science', 'Twitter', 'masters', 'computer', 'election', 'costly',\n",
    "        'learning', 'finance', 'machine', 'android', 'peace', 'nature', 'war']\n",
    "\n",
    "words_clusters = []\n",
    "embeddings_clusters = []\n",
    "\n",
    "for word in keys:\n",
    "    \n",
    "    words = []\n",
    "    embeddings = []\n",
    "    \n",
    "    for similar_word, _ in Word2VecModel.most_similar(word, topn = 30):\n",
    "        words.append(similar_word)\n",
    "        embeddings.append(Word2VecModel[word])\n",
    "    words_clusters.append(words)\n",
    "    embeddings_clusters.append(embeddings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b196f4fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:36:03.189193Z",
     "iopub.status.busy": "2021-12-07T14:36:03.188073Z",
     "iopub.status.idle": "2021-12-07T14:36:07.319556Z",
     "shell.execute_reply": "2021-12-07T14:36:07.320222Z",
     "shell.execute_reply.started": "2021-12-07T14:11:27.983550Z"
    },
    "papermill": {
     "duration": 4.221713,
     "end_time": "2021-12-07T14:36:07.320420",
     "exception": false,
     "start_time": "2021-12-07T14:36:03.098707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "    \n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "embedding_array = np.array(embeddings_clusters)\n",
    "n, m, k = embedding_array.shape\n",
    "\n",
    "tsne_2d_model = TSNE(perplexity = 15, n_components = 2, n_iter = 4000, random_state = 11, init = 'pca')\n",
    "tsne_embeddings = np.array(tsne_2d_model.fit_transform(embedding_array.reshape(n * m, k))).reshape(n, m, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d6dbb523",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:36:07.528137Z",
     "iopub.status.busy": "2021-12-07T14:36:07.527411Z",
     "iopub.status.idle": "2021-12-07T14:36:07.563078Z",
     "shell.execute_reply": "2021-12-07T14:36:07.563738Z",
     "shell.execute_reply.started": "2021-12-07T14:11:32.066008Z"
    },
    "papermill": {
     "duration": 0.145254,
     "end_time": "2021-12-07T14:36:07.563934",
     "exception": false,
     "start_time": "2021-12-07T14:36:07.418680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "    \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_most_similar_words(labels, embedding_cluster, word_cluster, title):\n",
    "    \n",
    "    colors = cm.rainbow(np.linspace(0, 1, len(labels)))\n",
    "    plt.figure(figsize = (16,9))\n",
    "    for label, embeddings, words, color in zip(labels, embedding_cluster, word_cluster, colors):\n",
    "        x = embeddings[:, 0]\n",
    "        y = embeddings[:, 1]\n",
    "        plt.scatter(x, y, c=color, alpha=0.7, label=label)\n",
    "    plt.legend(loc = 4)\n",
    "    plt.title(title)\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9aac1023",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T14:36:07.773186Z",
     "iopub.status.busy": "2021-12-07T14:36:07.772447Z",
     "iopub.status.idle": "2021-12-07T14:36:08.476022Z",
     "shell.execute_reply": "2021-12-07T14:36:08.476538Z",
     "shell.execute_reply.started": "2021-12-07T14:11:32.080852Z"
    },
    "papermill": {
     "duration": 0.813786,
     "end_time": "2021-12-07T14:36:08.476705",
     "exception": false,
     "start_time": "2021-12-07T14:36:07.662919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_most_similar_words(keys, tsne_embeddings, words_clusters, \"Visualizing Word2vec Word Embedding\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0ab206",
   "metadata": {
    "papermill": {
     "duration": 0.083427,
     "end_time": "2021-12-07T14:36:08.645420",
     "exception": false,
     "start_time": "2021-12-07T14:36:08.561993",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can visualize and analyze every embedding discussed in this notebook and can also suggest to me some new visualization techniques for Word Embeddings."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
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