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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/FrancescoSarandrea/deeplearning/blob/main/Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Iniziamo installando delle linrerie che fanno _scraping_ da Twitter; utilizzeremo la libreria **Transformer**, che ci permette di avere accesso a tuttti i modelli di analisi."
      ],
      "metadata": {
        "id": "Qa0aNdjT16FJ"
      }
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        "outputId": "60459055-4aea-49f5-90ca-980c4567e72c"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.20.1-py3-none-any.whl (4.4 MB)\n",
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            "  Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n",
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            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
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            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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            "Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.8.1 pyyaml-6.0 tokenizers-0.12.1 transformers-4.20.1\n"
          ]
        }
      ],
      "source": [
        "!pip install snscrape\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import snscrape.modules.twitter as sntwitter\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import transformers \n",
        "from transformers import pipeline\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "YF7j2hm92WsF"
      },
      "execution_count": null,
      "outputs": []
    },
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    {
      "cell_type": "markdown",
      "source": [
        "Scriviamo una funzione per scaricare i tweet"
      ],
      "metadata": {
        "id": "RJCMMT2h3cqw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# la funzione scarica un certo numero di tweet che contengono uno \n",
        "# specifico search term e sono nel periodo indicato\n",
        "def tweets(n_tweets,search_term,start_date,end_date):\n",
        "  tweets_list2=[]\n",
        "  for i,tweet in enumerate(sntwitter.TwitterSearchScraper(f'{search_term} since:{start_date} until {end_date}').get_items()):\n",
        "    if i>n_tweets:\n",
        "      break\n",
        "    tweets_list2.append([tweet.date,tweet.id,tweet.content])\n",
        "  tweet_df=pd.DataFrame(tweets_list2,columns=['Datetime','Tweet_id','Text'])\n",
        "  return tweet_df"
      ],
      "metadata": {
        "id": "uZMyWfx53P7S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_camille=tweets(5000,'camille vasquez','2022-05-20','2022-05-30')\n",
        "df_camille"
      ],
      "metadata": {
        "colab": {
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          "height": 49
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        "id": "O3J6YvlL6SSW",
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              "            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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df_camille.assign(Text=lambda x: x['Text'].str.lower())\n",
        "df=df.loc[df['Text'].str.contains('amber|heard|johnny|depp|shannon|curry')]"
      ],
      "metadata": {
        "id": "fW59jOqf8ZeU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "qcu9LyjE9KqW",
        "outputId": "c3bd5569-4f48-422f-e7d0-cb8ee358bc86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Datetime, Tweet_id, Text]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-842402b2-8c25-4525-99e4-44b7a24dc447\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Datetime</th>\n",
              "      <th>Tweet_id</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-842402b2-8c25-4525-99e4-44b7a24dc447')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-842402b2-8c25-4525-99e4-44b7a24dc447 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-842402b2-8c25-4525-99e4-44b7a24dc447');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# rimuoviamo gli URL dai tweet\n",
        "df=df.assign(Text=lambda x:x['Text'].apply(lambda s: re.sub(r'\\w\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+*(?:(\\/[^\\s/]*))*','',s)))"
      ],
      "metadata": {
        "id": "ieoF5W8h9LnL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tronchiamo il testo a 500 caratteri"
      ],
      "metadata": {
        "id": "Z6s8hgJf-pL9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Text']=df['Text'].apply(lambda s: s[0:500])"
      ],
      "metadata": {
        "id": "eA51FzkG9-vB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scriviamo la funzione che estragga il sentiment dai tweet"
      ],
      "metadata": {
        "id": "Dt-sconj-Kz7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.assign(sentim=lambda x: x['Text'].apply(lambda s:sentiment(s)))"
      ],
      "metadata": {
        "id": "1dYmO60A-J27"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.assign(sentim_label=lambda x: x['sentim'].apply(lambda s:s[0]['label']))\n",
        "df=df.assign(sentim_label=lambda x: x['sentim'].apply(lambda s:s[0]['score']))"
      ],
      "metadata": {
        "id": "p4fliGrg-dgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "BbS6PxOzA22T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['sentiment_label'].value_counts()"
      ],
      "metadata": {
        "id": "aUV_q_AxEJi1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}