data_preparation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preparation**"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "uYxw10gqFKER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import platform\n",
        "import os\n",
        "import sys\n",
        "import re\n",
        "import time\n",
        "import timeit\n",
        "from datetime import datetime, timedelta, date\n",
        "import glob\n",
        "import configparser\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.manifold import TSNE\n",
        "from collections import Counter\n",
        "import plotly.graph_objects as go\n",
        "#import fasttext\n",
        "from gensim.models.word2vec import Word2Vec\n",
        "from gensim.models.fasttext import FastText"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "gather": {
          "logged": 1688988596111
        },
        "jupyter": {
          "outputs_hidden": false
        },
        "id": "26idpLMqFKEY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "limit_corpus = 10000\n",
        "seed = 42"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988599701
        },
        "id": "YUnOlbFKFKEa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config = configparser.RawConfigParser()\n",
        "config.read('config.ini')\n",
        "\n",
        "#uri = config.get('MS AZURE', 'URI')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988601669
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJnji0rnFKEb",
        "outputId": "501c8c4e-6d73-4b6a-fbd0-900f48522f1d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "qew-UV-mFKEc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "7405VAp9FtTG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colab_active = 'google.colab' in sys.modules\n",
        "print(\"Colab Active: \", colab_active)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bN_s8pEYe4KM",
        "outputId": "9f8b3579-76d9-4a59-bd66-fd329484644a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab Active:  True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Downloads**"
      ],
      "metadata": {
        "id": "D88LTU0dF7Mt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if colab_active is True:\n",
        "  OS = platform.system()      # Operating System\n",
        "\n",
        "  def data_download(file_to_download, gdrive_code, OS, uncompress = True):\n",
        "    if not os.path.exists(file_to_download):\n",
        "      os.system('gdown --id \"'+gdrive_code+'\" --output '+file_to_download)\n",
        "      if OS == \"Linux\" and uncompress:\n",
        "          os.system('unzip -o -n \"./'+file_to_download+'\" -d '+os.path.dirname(file_to_download))\n",
        "      return True\n",
        "    else:\n",
        "      return None"
      ],
      "metadata": {
        "id": "AiO0GMfDF-PB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if colab_active is True:\n",
        "  out = data_download(\"./Senntiment_Social_Mood_Corpus_Tweets.zip\", \"11lopgQpkQ-UjwP62icelthzISG_fn2_5\", OS)"
      ],
      "metadata": {
        "id": "-1LuDcSUGBWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Training Set**\n",
        "Build the model training set starting from the sentipolc and happy parents datasets."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "XGAYP3a1FKEd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sentipolc"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "piamszVIFKEe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentipolc = pd.read_csv(f\"./Senntiment_Social_Mood_Corpus_Tweets/training_set_sentipolc16.csv\")\n",
        "colnames_sentipolc = list(train_sentipolc.columns)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988607195
        },
        "id": "ePam0De7FKEe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_sentipolc = pd.read_csv((f\"./Senntiment_Social_Mood_Corpus_Tweets/test_set_sentipolc16_gold2000.csv\"), on_bad_lines='skip', names = colnames_sentipolc)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988611627
        },
        "id": "VxkaSZp0FKEg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentipolc = pd.concat([train_sentipolc, test_sentipolc])"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988613950
        },
        "id": "-GtuGD82FKEh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentipolc['pol'] = 0\n",
        "\n",
        "sentipolc.loc[(sentipolc['opos'] == 1) & (sentipolc['oneg'] == 0), 'pol'] = int(1)\n",
        "sentipolc.loc[(sentipolc['opos'] == 0) & (sentipolc['oneg'] == 1), 'pol'] = int(2)\n",
        "sentipolc.loc[(sentipolc['opos'] == 0) & (sentipolc['oneg'] == 0), 'pol'] = int(0)\n",
        "sentipolc.loc[(sentipolc['opos'] == 1) & (sentipolc['oneg'] == 1), 'pol'] = int(3)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988615635
        },
        "id": "qT42M4FCFKEj"
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    },
    {
      "cell_type": "code",
      "source": [
        "sentipolc = sentipolc[-((sentipolc.opos==1) & (sentipolc.oneg==1))]\n",
        "sentipolc = sentipolc.drop(['idtwitter','subj','iro','lpos','lneg','top','opos','oneg'], axis = 1)\n",
        "\n",
        "class_counts_sp = sentipolc['pol'].value_counts()"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988617525
        },
        "id": "WHPLgYAaFKEk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'SENTIPOLC DF: \\n - Positive: {class_counts_sp[1]} \\n - Negative: {class_counts_sp[2]} \\n - Neutral:  {class_counts_sp[0]}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SENTIPOLC DF: \n",
            " - Positive: 1919 \n",
            " - Negative: 3253 \n",
            " - Neutral:  3705\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988619271
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIQqi7z2FKEl",
        "outputId": "005f2f18-0548-46d6-f795-d0d95c4a4982"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Happy Parents"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "DEjnMguoFKEm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy_parents = pd.read_csv(f\"./Senntiment_Social_Mood_Corpus_Tweets/gold_HappyParents.csv\")"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988621670
        },
        "id": "JU9MjbF9FKEn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy_parents.loc[happy_parents['polarity'] == 'POS', 'pol'] = int(1)\n",
        "happy_parents.loc[happy_parents['polarity'] == 'HUMPOS', 'pol'] = int(1)\n",
        "happy_parents.loc[happy_parents['polarity'] == 'NEG', 'pol'] = int(2)\n",
        "happy_parents.loc[happy_parents['polarity'] == 'HUMNEG', 'pol'] = int(2)\n",
        "happy_parents.loc[happy_parents['polarity'] == 'MIXED', 'pol'] = int(0)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988622577
        },
        "id": "DoU0u16JFKEn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy_parents.drop(happy_parents[happy_parents['polarity'] == 'NONE'].index, inplace = True)\n",
        "\n",
        "class_counts_hp = happy_parents['pol'].value_counts()"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988624672
        },
        "id": "nODewa_kFKEn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'HAPPY PARENTS DF: \\n - Positive: {class_counts_hp[1]} \\n - Negative: {class_counts_hp[2]} \\n - Neutral:  {class_counts_hp[0]}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HAPPY PARENTS DF: \n",
            " - Positive: 642 \n",
            " - Negative: 672 \n",
            " - Neutral:  28\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988627043
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PkfioGGFKEo",
        "outputId": "38d658c0-c40a-4650-f482-9a7eaa820d99"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### FeelIt"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "sSYhegLtFKEo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feelit = pd.read_csv(f\"./Senntiment_Social_Mood_Corpus_Tweets/feelit.csv\")"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988629402
        },
        "id": "wUJ95GVZFKEp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentipolc"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                                   text  pol\n",
              "0     Intanto la partita per Via Nazionale si compli...    2\n",
              "1     False illusioni, sgradevoli realtà Mario Monti...    2\n",
              "2     False illusioni, sgradevoli realtà #editoriale...    2\n",
              "3     Mario Monti: Berlusconi risparmi all'Italia il...    2\n",
              "4     Mario Monti: Berlusconi risparmi all'Italia il...    2\n",
              "...                                                 ...  ...\n",
              "1937  @serracchiani @pdnetwork solo che poi arriva @...    2\n",
              "1938  Anche prodotti alimentari tipici pugliesi in v...    0\n",
              "1939         intensità di vita  https://t.co/jv4aARxzhz    0\n",
              "1941  @GliIntoccabili @nonleggerlo Ma Ferrero? il co...    2\n",
              "1942  Non vedi l'ora che venga qui, almeno lo sentir...    1\n",
              "\n",
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              "      <td>Mario Monti: Berlusconi risparmi all'Italia il...</td>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8364bb2d-36a6-4f1d-8300-f340c1255692 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8364bb2d-36a6-4f1d-8300-f340c1255692');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988631324
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7Ixt8L9mFKEp",
        "outputId": "c15d0ef4-4a41-452e-8bf0-6bfefb7de5d4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feelit['pol'] = feelit['label'].replace(-1, 2)\n",
        "feelit['text'] = feelit['textClean']\n",
        "\n",
        "feelit.drop(columns = ['Unnamed: 0', 'label', 'textClean', 'Words'], inplace = True)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988633849
        },
        "id": "foWAtnipFKEq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Full Training Set"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "-n8TimTKFKEq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_df = pd.concat([sentipolc, happy_parents, feelit], ignore_index = True)\n",
        "training_df.drop(columns = ['polarity', 'sub_topic'], inplace = True)\n",
        "training_df['pol'] = training_df['pol'].astype('int')\n",
        "\n",
        "class_counts = training_df['pol'].value_counts()"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988636439
        },
        "id": "39FTrFYPFKEq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'FULL TRAINING DF: \\n - Positive: {class_counts[1]} \\n - Negative: {class_counts[2]} \\n - Neutral:  {class_counts[0]}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FULL TRAINING DF: \n",
            " - Positive: 3289 \n",
            " - Negative: 5234 \n",
            " - Neutral:  3733\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988638269
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4iZ07NMFKEr",
        "outputId": "1f25a9ae-0b74-49f2-ab57-74f0ff31f756"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "v6N2ZAG5FKEs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Text Preprocessing**"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "SfMZUvzSFKEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(training_df)):\n",
        "  training_df['text'][i]=re.sub(r'http\\S+','URL',training_df['text'][i])\n",
        "  training_df['text'][i]=re.sub(r'@\\S+','TAG',training_df['text'][i])"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": true
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988647562
        },
        "id": "9HlIUiWuFKEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_df.drop_duplicates(subset = ['text'], inplace = True)\n",
        "class_counts_clean = training_df.pol.value_counts()"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988650312
        },
        "id": "49EcjS2FFKEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'FULL TRAINING DF (CLEAN): \\n - Positive: {class_counts_clean[1]} \\n - Negative: {class_counts_clean[2]} \\n - Neutral:  {class_counts_clean[0]}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FULL TRAINING DF (CLEAN): \n",
            " - Positive: 3279 \n",
            " - Negative: 5212 \n",
            " - Neutral:  3710\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988652258
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7lF66KkFKEu",
        "outputId": "926e9625-2696-4111-d502-10ae0409dcdf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_df.to_csv('training_set.csv', sep = ';', encoding = 'utf-8')"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988654728
        },
        "id": "nUzHpP_sFKEu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "kXbHDrTeFKEv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Word Embedding Model**"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "uu4xKi_TFKEv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import gensim\n",
        "tf.config.experimental.set_memory_growth(tf.config.list_physical_devices('GPU')[0], True)"
      ],
      "metadata": {
        "id": "YNvH5QTYafoL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GPUWord2VecCallback(gensim.models.callbacks.CallbackAny2Vec):\n",
        "    def on_epoch_start(self, model):\n",
        "        model.trainables.syn1neg = tf.convert_to_tensor(model.trainables.syn1neg, dtype=tf.float32)\n",
        "        model.trainables.syn1 = tf.convert_to_tensor(model.trainables.syn1, dtype=tf.float32)"
      ],
      "metadata": {
        "id": "zsRDW_7vauuq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_date: date = date(2020,1,1)\n",
        "end_date  : date = date(2020,5,31)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988657280
        },
        "id": "lijnUI0ZFKEw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "date_list = []\n",
        "\n",
        "current_date = start_date\n",
        "iteration = 1\n",
        "\n",
        "while current_date <= end_date:\n",
        "    date_list.append(f'{current_date.year}-{current_date.month:02d}-{current_date.day:02d}')\n",
        "    current_date += timedelta(days=1)\n",
        "    iteration += 1"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988658854
        },
        "id": "FOXhMptoFKEw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "import tweet corpus"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "mJZqrESQFKEw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfs = []\n",
        "\n",
        "for date in date_list:\n",
        "    df_date = pd.read_csv(f'./Senntiment_Social_Mood_Corpus_Tweets/corpus_tweets/{date}.csv', sep=';', encoding='ISO-8859-1')\n",
        "    dfs.append(df_date)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988733219
        },
        "id": "RjXXuRnbFKEw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = pd.concat(dfs, ignore_index = True)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988736567
        },
        "id": "iXFZQbKIFKEx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the unsupervised word embedding model"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "jak7GTGKFKEx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if limit_corpus is not None:\n",
        "    sentences = raw_df['testo'].sample(limit_corpus, random_state = seed)\n",
        "else:\n",
        "    sentences = raw_df['testo']"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988760354
        },
        "id": "BlWH5eCqFKEy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_lists = list(map(lambda text: str(text).split(), sentences.values))"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688988847935
        },
        "id": "q2JSxD48FKEy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = timeit.default_timer()\n",
        "embedding_model = Word2Vec(word_lists, vector_size=300, window=8, min_count=1, workers=4, sg=0, epochs=1)\n",
        "\n",
        "\n",
        "#embedding_model = Word2Vec(word_lists, vector_size=300, window=8, min_count=1, workers=1, compute_loss=True)\n",
        "#embedding_model.build_vocab(word_lists)\n",
        "#embedding_model.train(word_lists, total_examples=embedding_model.corpus_count, epochs=30, compute_loss=True, callbacks=[GPUWord2VecCallback()])\n",
        "\n",
        "print(\"Elapsed Embeddings Training Time: \", timeit.default_timer()-start_time)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elapsed Embeddings Training Time:  1.6366459350001605\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": true
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688979836164
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_AhfsCPFKEz",
        "outputId": "501d0cb8-e54b-4051-f9fc-8f05c7533d33"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_model.save(\"corpus_2_not_utf_w2v.bin\")"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688979879116
        },
        "id": "2Xs-ZdjHFKEz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gensim fasttext"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUwgBBRncyyG",
        "outputId": "114e1100-4883-4348-c931-df83733363f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.1)\n",
            "Requirement already satisfied: fasttext in /usr/local/lib/python3.10/dist-packages (0.9.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.10.1)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (6.3.0)\n",
            "Requirement already satisfied: pybind11>=2.2 in /usr/local/lib/python3.10/dist-packages (from fasttext) (2.10.4)\n",
            "Requirement already satisfied: setuptools>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from fasttext) (67.7.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import fasttext\n",
        "import gensim"
      ],
      "metadata": {
        "id": "cBSDKRECcvxA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = timeit.default_timer()\n",
        "fasttext_model = FastText(word_lists, vector_size=300, window=8, min_count=1, workers=4, sg=0, epochs=1)\n",
        "#fasttext_model = fasttext.train_unsupervised(word_lists, epoch=1, ws=8, min_count = 1, dim=300)\n",
        "#word_repr=fasttext.train_unsupervised(word_lists,'cbow', dim=300, epoch=1)\n",
        "print(\"Elapsed Embeddings Training Time: \", timeit.default_timer()-start_time)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elapsed Embeddings Training Time:  23.393442440999934\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688980104509
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GQd1IS3FKE0",
        "outputId": "81999c2b-284d-4467-c79f-70729a4319c4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fasttext_model.save(\"corpus_2_not_utf.bin\")"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688980166812
        },
        "id": "kUdCYzsVFKE0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualize the embedding space"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "id": "ReNqBMn2FKE1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def embedding_space_visualization(root_word, model, plot_3d = True, plot_2d = False, topn = 20):\n",
        "\n",
        "  fig = plt.figure(figsize=(20, 17), dpi=80)\n",
        "  tot_words = [root_word] + [tupla[0] for tupla in model.wv.most_similar(root_word, topn=topn)]\n",
        "  X = model.wv[tot_words]\n",
        "\n",
        "  if plot_3d:\n",
        "    pca = TSNE(perplexity=topn, n_components=3, init='pca')\n",
        "\n",
        "    X = pca.fit_transform(X)\n",
        "    pca_data = pd.DataFrame(X, index=tot_words, columns=[\"x\",\"y\",\"z\"])\n",
        "    pca_data[\"input\"] = 0\n",
        "    pca_data[\"input\"].iloc[0:1] = 1\n",
        "\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "    ax.scatter(pca_data[pca_data[\"input\"]==0]['x'], pca_data[pca_data[\"input\"]==0]['y'], pca_data[pca_data[\"input\"]==0]['z'], c=\"black\")\n",
        "    ax.scatter(pca_data[pca_data[\"input\"]==1]['x'], pca_data[pca_data[\"input\"]==1]['y'], pca_data[pca_data[\"input\"]==1]['z'], c=\"red\")\n",
        "    ax.set(xlabel=None, ylabel=None, zlabel=None, xticklabels=[], yticklabels=[], zticklabels=[])\n",
        "\n",
        "    for label, row in pca_data[[\"x\",\"y\",\"z\"]].iterrows():\n",
        "        x, y, z = row\n",
        "        ax.text(x, y, z, s=label)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1688980472205
        },
        "id": "In3e9rYpFKE1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_space_visualization(\"covid\", fasttext_model, topn = 30)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1360 with 1 Axes>"
            ],
            "image/png": 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          "metadata": {}
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
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          "outputs_hidden": false
        },
        "nteract": {
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        },
        "gather": {
          "logged": 1688980521689
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wNDGNvQhFKE2",
        "outputId": "47f99cb8-118d-4041-fc01-c79ee1a7ca7a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>"
      ],
      "metadata": {
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          }
        },
        "id": "vJ27gR3sFKE3"
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    }
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  "metadata": {
    "kernelspec": {
      "name": "python310-sdkv2",
      "language": "python",
      "display_name": "Python 3.10 - SDK v2"
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    "language_info": {
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      "mimetype": "text/x-python",
      "codemirror_mode": {
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
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    "microsoft": {
      "ms_spell_check": {
        "ms_spell_check_language": "en"
      },
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
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    "nteract": {
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    "kernel_info": {
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    "colab": {
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      "gpuType": "T4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}