emotional_detection_with_data_fasttext.ipynb

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  "metadata": {
    "colab": {
      "name": "emotional detection with data fasttext",
      "provenance": [],
      "authorship_tag": "ABX9TyMmfxYs8mUYacrFY1yVf3Od",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hammedb197/100-Days-Of-ML-Code/blob/master/emotional_detection_with_data_fasttext.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjWMPlkRfmUN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cat 'dataset.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oURg9DQufmby",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUb-Uhp6f2hz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv('dataset.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z43Nz6LAf6I3",
        "colab_type": "code",
        "outputId": "0b2ab3d1-4d24-4217-8504-928677ba11bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>joy</td>\n",
              "      <td>On days when I feel close to my partner and ot...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fear</td>\n",
              "      <td>Every time I imagine that someone I love or I ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>anger</td>\n",
              "      <td>When I had been obviously unjustly treated and...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sadness</td>\n",
              "      <td>When I think about the short time that we live...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>disgust</td>\n",
              "      <td>At a gathering I found myself involuntarily si...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     label                                               text Unnamed: 2\n",
              "0      joy  On days when I feel close to my partner and ot...        NaN\n",
              "1     fear  Every time I imagine that someone I love or I ...        NaN\n",
              "2    anger  When I had been obviously unjustly treated and...        NaN\n",
              "3  sadness  When I think about the short time that we live...        NaN\n",
              "4  disgust  At a gathering I found myself involuntarily si...        NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_ZFco_Yl5N5",
        "colab_type": "code",
        "outputId": "b21c7a0a-19c7-4443-e230-e87203c85196",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "data.label.value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "joy        1092\n",
              "sadness    1082\n",
              "anger      1079\n",
              "fear       1076\n",
              "shame      1071\n",
              "disgust    1066\n",
              "guilt      1050\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8sA9kuF5T-z",
        "colab_type": "code",
        "outputId": "fea07f2e-a06b-4254-bf32-85a69f27b060",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FalTrhuBl5RR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "import re\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemma = WordNetLemmatizer()\n",
        "stopwords = stopwords.words('english')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPE-xLiul5Ts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def clean_data(data): \n",
        "  clean_data = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|(RT)|(http[:/.A-Za-z0-9]+)|(\\W)|([0-9])\", \" \", data)\n",
        "  token_data = word_tokenize(clean_data)\n",
        "  lemma_data = [lemma.lemmatize(i) for i in token_data]\n",
        "  stopword_data = ' '.join([i for i in lemma_data if i not in stopwords])\n",
        "  return stopword_data\n",
        "\n",
        "# (@)|(http[:/.A-Za-z0-9]+)|(RT)|(#)|(\\W)|([0-9])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdSLBnJkl5Vn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['text'] = data['text'].apply(clean_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5Jw02cbWgFO",
        "colab_type": "code",
        "outputId": "2ed8f829-a033-45cd-e677-d29e0df5c1de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data['label'].isnull().sum()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "myf6xseTWtXl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['label'] = \"__label__\" + data['label']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-63hF33irq4H",
        "colab_type": "text"
      },
      "source": [
        "labels start by the __label__ prefix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbqpFdewl5YR",
        "colab_type": "code",
        "outputId": "0386f995-63c6-4674-f0dd-dc599da946d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>__label__joy</td>\n",
              "      <td>On day I feel close partner friend When I feel...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>__label__fear</td>\n",
              "      <td>Every time I imagine someone I love I could co...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>__label__anger</td>\n",
              "      <td>When I obviously unjustly treated possibility ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>__label__sadness</td>\n",
              "      <td>When I think short time live relate period lif...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>__label__disgust</td>\n",
              "      <td>At gathering I found involuntarily sitting nex...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              label  ... Unnamed: 2\n",
              "0      __label__joy  ...        NaN\n",
              "1     __label__fear  ...        NaN\n",
              "2    __label__anger  ...        NaN\n",
              "3  __label__sadness  ...        NaN\n",
              "4  __label__disgust  ...        NaN\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHuK_NEP62DG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.iloc[:,  :2]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVa3f_k162Hg",
        "colab_type": "code",
        "outputId": "4a555aed-73f9-4290-e395-480ad8cb42d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>__label__joy</td>\n",
              "      <td>On day I feel close partner friend When I feel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>__label__fear</td>\n",
              "      <td>Every time I imagine someone I love I could co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>__label__anger</td>\n",
              "      <td>When I obviously unjustly treated possibility ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>__label__sadness</td>\n",
              "      <td>When I think short time live relate period lif...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>__label__disgust</td>\n",
              "      <td>At gathering I found involuntarily sitting nex...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              label                                               text\n",
              "0      __label__joy  On day I feel close partner friend When I feel...\n",
              "1     __label__fear  Every time I imagine someone I love I could co...\n",
              "2    __label__anger  When I obviously unjustly treated possibility ...\n",
              "3  __label__sadness  When I think short time live relate period lif...\n",
              "4  __label__disgust  At gathering I found involuntarily sitting nex..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SItuzHVnXaay",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a1554e71-55ba-4ceb-c18a-c04ba888f6e2"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7516, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hX2KA7yryRd",
        "colab_type": "text"
      },
      "source": [
        " full dataset contains 7516 examples, 5637 train set, 1879 tst set \n",
        " \n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BR2iatCg5Wx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "461ccc6c-db83-4b46-a6a2-ed7a7f4d2717"
      },
      "source": [
        "75/100 * 7516"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5637.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s87PSjt1hCBQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "acd80481-de02-41c9-918b-2cdc4dff7c5f"
      },
      "source": [
        "7516 - 5637"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1879"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDrAevzCX5wU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.to_csv(r'data.txt', header=None, index=None, sep=' ', mode='a')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCc2aeg7X5zR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf3a7f78-b0f7-48a1-c4c3-833478d20c19"
      },
      "source": [
        "!wc data.txt"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  7516 103653 692855 data.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYeRqyxDX51V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!head -n 5637 data.txt > data.train\n",
        "!tail -n 1879 data.txt > data.valid"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVxQ3z2BqmrD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "d866372c-cd78-4d86-cd66-fb8b75267592"
      },
      "source": [
        "!pip install fasttext"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fasttext\n",
            "  Using cached https://files.pythonhosted.org/packages/f8/85/e2b368ab6d3528827b147fdb814f8189acc981a4bc2f99ab894650e05c40/fasttext-0.9.2.tar.gz\n",
            "Requirement already satisfied: pybind11>=2.2 in /usr/local/lib/python3.6/dist-packages (from fasttext) (2.5.0)\n",
            "Requirement already satisfied: setuptools>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from fasttext) (46.1.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from fasttext) (1.18.3)\n",
            "Building wheels for collected packages: fasttext\n",
            "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fasttext: filename=fasttext-0.9.2-cp36-cp36m-linux_x86_64.whl size=3016139 sha256=0879c4611db613eb6d32a4615a220b10fe9e2f5ddd2d675830575c6795a2ce1b\n",
            "  Stored in directory: /root/.cache/pip/wheels/98/ba/7f/b154944a1cf5a8cee91c154b75231136cc3a3321ab0e30f592\n",
            "Successfully built fasttext\n",
            "Installing collected packages: fasttext\n",
            "\u001b[31mERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: '/usr/local/lib/python3.6/dist-packages/fasttext-0.9.2.dist-info/INSTALLER.pip'\n",
            "\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvIq0YEbXLtU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import fasttext"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1d1scW7XNav",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = fasttext.train_supervised(input=\"data.train\", epoch=25, wordNgrams=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojcpL6LcXNgO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_model('model.bin')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bAXex03nXNje",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict = model.predict('I am happy today')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CT2oLkqZ62Lb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1cceffac-1ec9-46b2-8435-a9cc4368663d"
      },
      "source": [
        "model.test(\"data.valid\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1879, 0.5705162320383182, 0.5705162320383182)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKPkrcdS62Sr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1b2a0192-f2c5-4b36-d357-52b6acd5eaca"
      },
      "source": [
        "predict[0][0].split(\"__label__\")[1]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'joy'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CRXLE82B1obd",
        "colab_type": "text"
      },
      "source": [
        "Test model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-GdQ70o1nW_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ed33aefb-bc01-4e09-d8a9-8b582bb8308c"
      },
      "source": [
        "model = fasttext.load_model('/content/model.bin')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9Ig0bHw1nfc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_emotion(data):\n",
        "  clean_data = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|(RT)|(http[:/.A-Za-z0-9]+)|(\\W)|([0-9])\", \" \", data)\n",
        "  token_data = word_tokenize(clean_data)\n",
        "  lemma_data = [lemma.lemmatize(i) for i in token_data]\n",
        "  stopword_data = ' '.join([i for i in lemma_data if i not in stopwords])\n",
        "  predict = model.predict(stopword_data)\n",
        "  output = predict[0][0].split(\"__label__\")[1]\n",
        "  return output\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjtxBMCq2Opz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d91c29a4-5d20-41bf-fe4b-a50623ed1a52"
      },
      "source": [
        "predict_emotion(\"basically, I am the only one with an ugly product flow on this teamYawning face.\")"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'guilt'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    }
  ]
}