HaSpeeDe.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0z1bi2_ig7_3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4bcb094-320b-4681-b696-a3d66ab38c2b"
      },
      "source": [
        "import tensorflow as tf\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "!pip install transformers\n",
        "!pip install bert-tensorflow\n",
        "!pip install imbalanced-learn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m55.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.1/200.1 kB\u001b[0m \u001b[31m26.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m73.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bert-tensorflow\n",
            "  Downloading bert_tensorflow-1.0.4-py2.py3-none-any.whl (64 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.4/64.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from bert-tensorflow) (1.16.0)\n",
            "Installing collected packages: bert-tensorflow\n",
            "Successfully installed bert-tensorflow-1.0.4\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.9/dist-packages (0.10.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from imbalanced-learn) (3.1.0)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.9/dist-packages (from imbalanced-learn) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.9/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.9/dist-packages (from imbalanced-learn) (1.22.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from imbalanced-learn) (1.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRSrY2V1tCtQ",
        "outputId": "fe11444f-53ee-4c89-a88b-ce212957a151"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive') \n",
        "model_save_name = 'alberto_hodi.pt'\n",
        "path = F\"/content/gdrive/My Drive/{model_save_name}\" "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-FA7-P7hpAw"
      },
      "source": [
        "#Training misogyny"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vsj0pwnmhMH7"
      },
      "source": [
        "data = pd.read_excel('haspeedee_train.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "2C0pod6OkV-G",
        "outputId": "c11e6802-0be0-4542-f0e3-ee2aafa90489"
      },
      "execution_count": null,
      "outputs": [
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              "      anonymized_tweet_id                                    anonymized_text  \\\n",
              "0         217874450618134  @unknown Con tutte le denunce che si sta becca...   \n",
              "1         360042217507605  #PRESCRIZIONE : I (t)re magi hanno trovato l’a...   \n",
              "2         817917484817935  Il #m5s ha votato contro le #unionicivili addu...   \n",
              "3         145836038456701  Che cosa cambia questa #legge? Caro @667517147...   \n",
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              "5036      715670659156960  #Covid. #Draghi non fare come #Conte. La #poli...   \n",
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              "5038      887061502405620  @401560935985106 @507718538254873 @28429789767...   \n",
              "5039      129706985183701                      ma chi cazzo è letta? \\n[URL]   \n",
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              "3         0  training_politics     train  \n",
              "4         0  training_politics     train  \n",
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              "5035      0  training_politics     train  \n",
              "5036      0  training_politics     train  \n",
              "5037      0  training_politics     train  \n",
              "5038      1  training_politics     train  \n",
              "5039      0  training_politics     train  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna()"
      ],
      "metadata": {
        "id": "2t_OEDQu6ktj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVHN0PD2hx9w"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset in traning and validation(test)\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(\n",
        "    data.index.values,\n",
        "    data.label.values,\n",
        "    test_size=0.06,\n",
        "    random_state=17,\n",
        "    stratify=data.label.values\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KthW1DPakQQ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2e223cef-bfc9-4c49-bbb1-63fb5c12b16d"
      },
      "source": [
        "# Check datasets composition\n",
        "data['data_type'] = ['not_set'] * data.shape[0]\n",
        "data.loc[X_train, 'data_type'] = 'train'\n",
        "data.loc[X_val, 'data_type'] = 'val'\n",
        "data.groupby(['label', 'data_type']).count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 anonymized_tweet_id  anonymized_text  dataset\n",
              "label data_type                                               \n",
              "0     train                     2923             2923     2923\n",
              "      val                        187              187      187\n",
              "1     train                     1814             1814     1814\n",
              "      val                        116              116      116"
            ],
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              "\n",
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              "      <th>dataset</th>\n",
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              "    <tr>\n",
              "      <th>val</th>\n",
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              "      <th>val</th>\n",
              "      <td>116</td>\n",
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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",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9wLRsJ2klqX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "be806501edf743b3b4c5d2aad63c3ebc",
            "1958419f7d4a4027b63fad09fd48a8df",
            "604e8eef63d448dd9fb042dfe2fe4353",
            "baa3406c9d7a493c800d78b61070963b",
            "5e3fabc92410430ea83c32ac88005162",
            "e383a0a61a074dbfbd66fa565bd5ee9a",
            "915be49419714ef4a9d9f5187d017c42",
            "0c61ce3a09554527bd319ecfec580e6b",
            "e4a778d2ecfe49f2ab82c61a3189a268",
            "ae6504df5a8948d08d3318ac1a0810d0",
            "b87fd36462bf49e4a2c789e43677c3dc",
            "d235b1bd3f5e41bfb467b99a22f85eae",
            "a5b8114a10d341579e734101346d9fa8",
            "cc8ee9ccae2447f98ccebdaa638e8b26",
            "0135e1a95f314be0be99ef703b91de2b",
            "7c9ac1dc1f1e46339fd74cde94b601da",
            "a1d72806150845268e0cbc1cfe9963f9",
            "ab3b59758bc84297914c6665e33b8e37",
            "db5e5929145a42768a19280d271f33be",
            "e4a5262ac19a4b4794dfb13f88bcca44",
            "4c47af94f5f5465295e1f85b719eb3cf",
            "9111c2e7418e4dc28d263dbe729641b8"
          ]
        },
        "outputId": "b9ac48e9-2f74-479a-c25f-acfcf6d90dbd"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "from torch.utils.data import TensorDataset\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0\", use_fast=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be806501edf743b3b4c5d2aad63c3ebc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/1.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d235b1bd3f5e41bfb467b99a22f85eae"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#change column name\n",
        "data = data.rename(columns={'anonymized_text' : 'text'})"
      ],
      "metadata": {
        "id": "LztbBk3IlRFp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O77qk3QZk-Jy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "457412e6-33c7-4895-c540-2ec20bc3ccd2"
      },
      "source": [
        "# Encode training dataset using the tokenizer\n",
        "encoded_data_train = tokenizer.batch_encode_plus(\n",
        "    data[data.data_type == 'train'].text.values,\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True, \n",
        "    pad_to_max_length=True,\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")\n",
        "\n",
        "# Encode validation dataset using the tokenizer\n",
        "encoded_data_val = tokenizer.batch_encode_plus(\n",
        "    data[data.data_type == 'val'].text.values,\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,  \n",
        "    pad_to_max_length=True,\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:2346: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSMqDTU9lCIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3adc7f97-93b7-4865-f133-bc5aded12047"
      },
      "source": [
        "# Extract IDs, attention masks and labels from training dataset\n",
        "input_ids_train = encoded_data_train['input_ids']\n",
        "attention_masks_train = encoded_data_train['attention_mask']\n",
        "labels_train = torch.tensor(data[data.data_type == 'train'].label.values)\n",
        "labels_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 0, 1,  ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kKdF5WylIEQ"
      },
      "source": [
        "# Extract IDs, attention masks and labels from validation dataset\n",
        "input_ids_val = encoded_data_val['input_ids']\n",
        "attention_masks_val = encoded_data_val['attention_mask']\n",
        "labels_val = torch.tensor(data[data.data_type == 'val'].label.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWUx3_CwlTjy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76224b29-0789-46f8-eb74-34852e5872f1"
      },
      "source": [
        "# Create train and validation dataset from extracted features\n",
        "dataset_train = TensorDataset(input_ids_train, attention_masks_train, labels_train)\n",
        "dataset_val = TensorDataset(input_ids_val, attention_masks_val, labels_val)\n",
        "print(\"Train dataset length: {}\\nValidation dataset length: {}\".format(len(dataset_train), len(dataset_val)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset length: 4737\n",
            "Validation dataset length: 303\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5-DYrbOlcoM"
      },
      "source": [
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
        "\n",
        "# Define the size of each batch\n",
        "batch_size = 16\n",
        "\n",
        "# Load training dataset\n",
        "dataloader_train= DataLoader(\n",
        "    dataset_train,\n",
        "    sampler=RandomSampler(dataset_train),\n",
        "    batch_size=batch_size)\n",
        "\n",
        "# Load valuation dataset\n",
        "dataloader_val= DataLoader(\n",
        "    dataset_val,\n",
        "    sampler=RandomSampler(dataset_val),\n",
        "    batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7U0CbPjlgur",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "referenced_widgets": [
            "0f2c42d3f9b94b7fbb1674dfd522cb30",
            "b7d275d82b4f46b287bb79eaa4d509d7",
            "e901a5e4da6644df8ebc68b023e85f24",
            "4bd93b7343a242ffba02352b4519abab",
            "ef863889b4bf43f2854ae014a9588c9e",
            "03b5cf38e6a7457c87771f00824128dd",
            "c055bb5c12114e9e8738305f354aef89",
            "c0d48d593aae4802863098695b8c5f1d",
            "da00708cf1ac4a619a5abee52a72d1bc",
            "3f2bf8099e6d4496bda0270433098751",
            "10bb3bcb6958438ea77e2f975689046b"
          ],
          "height": 160
        },
        "outputId": "72dff32f-8177-4133-9f32-8372aa8dc1cf"
      },
      "source": [
        "from transformers import BertForSequenceClassification\n",
        "# Load pre-trained BERT model\n",
        "model = BertForSequenceClassification.from_pretrained(\"m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/740M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f2c42d3f9b94b7fbb1674dfd522cb30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clFf9AhhlkPt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe23353e-6cf6-4aba-a537-d1be82996521"
      },
      "source": [
        "from transformers import AdamW, get_linear_schedule_with_warmup\n",
        "\n",
        "# Define model optimizer -> Adam\n",
        "optimizer = AdamW(\n",
        "    model.parameters(),\n",
        "    lr = 1e-5, \n",
        "    eps=1e-8\n",
        ")\n",
        "# Define model scheduler\n",
        "epochs = 4\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer,\n",
        "                                            num_warmup_steps=0,\n",
        "                                            num_training_steps=len(dataloader_train)*epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIhc3rohlsah"
      },
      "source": [
        "import random\n",
        "\n",
        "# Define random seeds\n",
        "seed_val = 17\n",
        "random.seed(seed_val)\n",
        "np.random.seed(seed_val)\n",
        "torch.manual_seed(seed_val)\n",
        "torch.cuda.manual_seed_all(seed_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgeQ8ClPl0en",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d23d30b-c9bf-47a8-dc98-0f2118e74b44"
      },
      "source": [
        "# Define processor type for torch\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HTc5Owyl2nu"
      },
      "source": [
        "from sklearn.metrics import f1_score, precision_score, recall_score\n",
        "\n",
        "# Returns the F1 score computed on the predictions\n",
        "def f1_score_func(preds, labels):\n",
        "    preds_flat=np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat=labels.flatten()\n",
        "    return f1_score(labels_flat, preds_flat, average='macro')\n",
        "\n",
        "\n",
        "\n",
        "# Returns the precision and recall score computed on the predictions\n",
        "def prec_func(preds, labels):\n",
        "    preds_flat=np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat=labels.flatten()\n",
        "    return precision_score(labels_flat, preds_flat, average='macro')\n",
        "\n",
        "def recall_func(preds, labels):\n",
        "    preds_flat=np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat=labels.flatten()\n",
        "    return recall_score(labels_flat, preds_flat, average='macro')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NngodYQl8eh"
      },
      "source": [
        "# Evaluates the model using the validation set\n",
        "def evaluate(dataloader_val):\n",
        "  model.eval()\n",
        "  loss_val_total = 0\n",
        "  predictions, true_vals = [], []\n",
        "\n",
        "  for batch in dataloader_val:\n",
        "      batch = tuple(b.to(device) for b in batch)\n",
        "      inputs = {'input_ids': batch[0],\n",
        "        'attention_mask': batch[1],\n",
        "        'labels': batch[2],\n",
        "        }\n",
        "\n",
        "      with torch.no_grad():\n",
        "          outputs = model(**inputs)\n",
        "\n",
        "      loss = outputs[0]\n",
        "      logits = outputs[1]\n",
        "      loss_val_total += loss.item()\n",
        "\n",
        "      logits = logits.detach().cpu().numpy()\n",
        "      label_ids = inputs['labels'].cpu().numpy()\n",
        "      predictions.append(logits)\n",
        "      true_vals.append(label_ids)\n",
        "\n",
        "  loss_val_avg = loss_val_total / len(dataloader_val)\n",
        "\n",
        "  predictions = np.concatenate(predictions, axis=0)\n",
        "  true_vals = np.concatenate(true_vals, axis=0)\n",
        "\n",
        "  return loss_val_avg, predictions, true_vals"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCi_tFYmmJkg",
        "colab": {
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        "outputId": "5a54f6ed-9337-482d-f525-d45f2f2d2bc8"
      },
      "source": [
        "for epoch in tqdm(range(1, epochs + 1)):\n",
        "\n",
        "    model.train()  # model is training\n",
        "\n",
        "    loss_train_total = 0\n",
        "\n",
        "    progress_bar = tqdm(dataloader_train, desc='Epoch {:1d}'.format(epoch), leave=False, disable=False)\n",
        "    for batch in progress_bar:\n",
        "        model.zero_grad()\n",
        "        batch = tuple(b.to(device) for b in batch)\n",
        "        inputs = {'input_ids': batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels': batch[2]}\n",
        "        \n",
        "        outputs = model(**inputs)\n",
        "\n",
        "        loss = outputs[0]\n",
        "        loss_train_total += loss.item()\n",
        "        loss.backward()  # to backpropagate\n",
        "\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(),\n",
        "                                      1.0)  # prevents the gradient from being too small or too big\n",
        "\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item() / len(batch))})\n",
        "\n",
        "    \n",
        "    torch.save(model, path)\n",
        "    tqdm.write(f'\\nEpoch {epoch}/{epochs}')\n",
        "\n",
        "    loss_train_avg = loss_train_total / len(dataloader_train)\n",
        "    tqdm.write(f'Training loss: {loss_train_avg}')  # make sure that model is still training\n",
        "\n",
        "    val_loss, predictions, true_vals = evaluate(dataloader_val)  # to check overtraining (or overfitting)\n",
        "    val_f1 = f1_score_func(predictions, true_vals)\n",
        "    val_prec = prec_func(predictions, true_vals)\n",
        "    val_recall = recall_func(predictions, true_vals)\n",
        "\n",
        "    tqdm.write(f'Validation loss: {val_loss}')\n",
        "    tqdm.write(f'F1 Score(weighted) : {val_f1}')\n",
        "    tqdm.write(f'Prec Score(weighted) : {val_prec}')\n",
        "    tqdm.write(f'Recall Score(weighted) : {val_recall}')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a30debd6c34441b2a7c94f99b1fde526"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 1:   0%|          | 0/297 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac9fae8952554070a99faf85c4889392"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1/4\n",
            "Training loss: 0.36340932112801755\n",
            "Validation loss: 0.25048830869950744\n",
            "F1 Score(weighted) : 0.8943336370692856\n",
            "Prec Score(weighted) : 0.889186200796108\n",
            "Recall Score(weighted) : 0.9056564632122441\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 2:   0%|          | 0/297 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3347021a81a84b9bbb6dc7a30c136ee4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 2/4\n",
            "Training loss: 0.21213836737152703\n",
            "Validation loss: 0.2450219426108034\n",
            "F1 Score(weighted) : 0.9100520643039824\n",
            "Prec Score(weighted) : 0.9068019201159315\n",
            "Recall Score(weighted) : 0.9141158030610363\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 3:   0%|          | 0/297 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed7e0a13dc6b4d1bbf073305b5183827"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 3/4\n",
            "Training loss: 0.15193000763046371\n",
            "Validation loss: 0.3018527997559623\n",
            "F1 Score(weighted) : 0.8992143143889431\n",
            "Prec Score(weighted) : 0.8974470222871758\n",
            "Recall Score(weighted) : 0.9011847685782777\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch 4:   0%|          | 0/297 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5ad68d6e2dd4310a97c2e9e251c50f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 4/4\n",
            "Training loss: 0.11706255062997517\n",
            "Validation loss: 0.3337841548613812\n",
            "F1 Score(weighted) : 0.9072543617998163\n",
            "Prec Score(weighted) : 0.9024695776664281\n",
            "Recall Score(weighted) : 0.9147151023418771\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained('content/gdrive/MyDrive/alberto_haspeede')"
      ],
      "metadata": {
        "id": "dWfiClit9O_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = []\n",
        "for i, prediction in enumerate(predictions):\n",
        "  predicted = np.argmax(prediction)\n",
        "  # print(f\"index: {i} -- prediction: {predicted}\")\n",
        "  results.append(predicted)"
      ],
      "metadata": {
        "id": "rFF06xs8wHzT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hate = []\n",
        "for prediction in results:\n",
        "  hate.append(prediction)"
      ],
      "metadata": {
        "id": "ADaPsMyPJ0RI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "LNew00clxcsn",
        "outputId": "34a85dfc-6fc7-4957-8995-1e42c843b48d"
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "      anonymized_tweet_id                                               text  \\\n",
              "0         217874450618134  @unknown Con tutte le denunce che si sta becca...   \n",
              "1         360042217507605  #PRESCRIZIONE : I (t)re magi hanno trovato l’a...   \n",
              "2         817917484817935  Il #m5s ha votato contro le #unionicivili addu...   \n",
              "3         145836038456701  Che cosa cambia questa #legge? Caro @667517147...   \n",
              "4         599642342348521  @800991101440753 Spero rientrino 2000 insegnan...   \n",
              "...                   ...                                                ...   \n",
              "5035      221538539806447  Sportello telematico attivo al Comune di Santa...   \n",
              "5036      715670659156960  #Covid. #Draghi non fare come #Conte. La #poli...   \n",
              "5037      512309888296620  Cina, «128mila euro a neonato»: la proposta pe...   \n",
              "5038      887061502405620  @401560935985106 @507718538254873 @28429789767...   \n",
              "5039      129706985183701                      ma chi cazzo è letta? \\n[URL]   \n",
              "\n",
              "      label            dataset data_type  \n",
              "0         1  training_politics     train  \n",
              "1         0  training_politics     train  \n",
              "2         1  training_politics     train  \n",
              "3         0  training_politics     train  \n",
              "4         0  training_politics     train  \n",
              "...     ...                ...       ...  \n",
              "5035      0  training_politics     train  \n",
              "5036      0  training_politics     train  \n",
              "5037      0  training_politics     train  \n",
              "5038      1  training_politics     train  \n",
              "5039      0  training_politics     train  \n",
              "\n",
              "[5040 rows x 5 columns]"
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              "      <th></th>\n",
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              "      <td>Il #m5s ha votato contro le #unionicivili addu...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>train</td>\n",
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              "      <td>Che cosa cambia questa #legge? Caro @667517147...</td>\n",
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              "      <td>training_politics</td>\n",
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              "    </tr>\n",
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              "      <td>Sportello telematico attivo al Comune di Santa...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>train</td>\n",
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              "      <th>5036</th>\n",
              "      <td>715670659156960</td>\n",
              "      <td>#Covid. #Draghi non fare come #Conte. La #poli...</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>887061502405620</td>\n",
              "      <td>@401560935985106 @507718538254873 @28429789767...</td>\n",
              "      <td>1</td>\n",
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              "      <td>129706985183701</td>\n",
              "      <td>ma chi cazzo è letta? \\n[URL]</td>\n",
              "      <td>0</td>\n",
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              "  "
            ]
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          "metadata": {},
          "execution_count": 25
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    {
      "cell_type": "markdown",
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        "#Preds sul val"
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      "metadata": {
        "id": "IHET-VO6gnkv"
      }
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    {
      "cell_type": "code",
      "source": [
        "data_val = data.loc[data['data_type'] == 'val']"
      ],
      "metadata": {
        "id": "HPskRcQSw-Z8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "data_val"
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        "id": "wR-IczcUxwnc",
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              "      anonymized_tweet_id                                               text  \\\n",
              "3         172580609652325  La #Lega e il #m5s stanno dando a bere all'opi...   \n",
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        "data_val['hate'] = hate"
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        "id": "nzbultYJw6CP",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-0caca47192c7>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_val['hate'] = hate\n"
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              "      anonymized_tweet_id                                               text  \\\n",
              "3         172580609652325  La #Lega e il #m5s stanno dando a bere all'opi...   \n",
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              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5588</th>\n",
              "      <td>517002584005250</td>\n",
              "      <td>#14maggio #PapaFrancesco Occorrono politiche f...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5598</th>\n",
              "      <td>531900858879345</td>\n",
              "      <td>Trasporto pubblico, Simeone nominato consiglie...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>560 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "          const element = document.querySelector('#df-306f2bd2-3ced-4fa1-b3d0-d1d2ecc04d4f');\n",
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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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_val.to_excel('HaSpeeDe_preds.xlsx', index=False)"
      ],
      "metadata": {
        "id": "9YIrQq8Qx6QG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preds sul test"
      ],
      "metadata": {
        "id": "evbNc9fCgqle"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgGSujtqmQdr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "04419b48-b2b3-46fb-9c02-f62f497a5a27"
      },
      "source": [
        "#Load test set\n",
        "data_test = pd.read_excel('haspeedee_test.xlsx')\n",
        "data_test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   anonymized_tweet_id                                    anonymized_text  \\\n",
              "0      172580609652325  La #Lega e il #m5s stanno dando a bere all'opi...   \n",
              "1      825279779951638  Il decreto #salvini crea insicurezza e disuman...   \n",
              "2      863998520896761  \"Paolo Polidori\" dovrebbe dimettersi per manif...   \n",
              "3      327244336972419  #ottoemezzo. #Bongiorno ci racconta che il gov...   \n",
              "4      897329723892923  #ottoemezzo #cacciari spiega alla #bongiorno l...   \n",
              "\n",
              "   label            dataset data_type  \n",
              "0      0  training_politics       val  \n",
              "1      0  training_politics       val  \n",
              "2      1  training_politics       val  \n",
              "3      0  training_politics       val  \n",
              "4      0  training_politics       val  "
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              "          const element = document.querySelector('#df-bfc47d3b-f39e-4e65-bc67-b1b9969ddfef');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTTUmewy7OR_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddc6c1f0-9fc2-40ab-8da6-9f8e98ad5588"
      },
      "source": [
        "# Encode validation dataset using the tokenizer\n",
        "encoded_data_test = tokenizer.batch_encode_plus(\n",
        "    data_test.anonymized_text.values,\n",
        "    add_special_tokens=True,\n",
        "    return_attention_mask=True,  \n",
        "    pad_to_max_length=True,\n",
        "    max_length=256,\n",
        "    return_tensors='pt'\n",
        ")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:2346: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAbCOHUT7uz3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4908fb44-ef89-4a00-c5a5-2d47580751ea"
      },
      "source": [
        "# Extract IDs, attention masks and labels from validation dataset\n",
        "input_ids_test = encoded_data_test['input_ids']\n",
        "attention_masks_test = encoded_data_test['attention_mask']\n",
        "\n",
        "dataset_test = TensorDataset(input_ids_test, attention_masks_test)\n",
        "print(\"Test dataset length: {}\".format(len(dataset_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test dataset length: 560\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NspWtIWC70Tw"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "dataloader_test = DataLoader(dataset_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5cY07TS73EV"
      },
      "source": [
        "# Evaluates the model using the validation set\n",
        "def predict(dataset_test):\n",
        "    predictions = []\n",
        "\n",
        "    for row in dataset_test:\n",
        "      row = tuple(r.to(device) for r in row)\n",
        "      inputs = {'input_ids': row[0],\n",
        "        'attention_mask': row[1]\n",
        "        }\n",
        "\n",
        "      with torch.no_grad():\n",
        "          outputs = model(**inputs)\n",
        "\n",
        "      logits = outputs[0]\n",
        "      logits = logits.detach().cpu().numpy()\n",
        "      predictions.append(logits)\n",
        "\n",
        "    return predictions\n",
        "\n",
        "# Predict values for test dataset\n",
        "predictions = predict(dataloader_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86U-GcDj75K2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e493bc1-0c69-4561-e2e6-7afadeba678b"
      },
      "source": [
        "print(len(predictions))\n",
        "results = []\n",
        "for i, prediction in enumerate(predictions):\n",
        "  predicted = np.argmax(prediction, axis=1)[0]\n",
        "  # print(f\"index: {i} -- prediction: {predicted}\")\n",
        "  results.append(predicted)\n",
        "\n",
        "print(results)\n",
        "print(results.count(0))\n",
        "print(results.count(1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "560\n",
            "[0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]\n",
            "340\n",
            "220\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIUFs0uM8M87"
      },
      "source": [
        "misogynous = []\n",
        "for prediction in results:\n",
        "  misogynous.append(prediction)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7_oAAdW8VmE"
      },
      "source": [
        "#Predict misogyny\n",
        "data_test['preds'] = misogynous\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9HaHxtNqgo8"
      },
      "source": [
        "#Save the predicted labels in a csv file \n",
        "data_test.to_excel('preds_haspeedee_test.xlsx', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Testing "
      ],
      "metadata": {
        "id": "18EWB5NhLS3j"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVr7JmVdIge9"
      },
      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hs = pd.read_excel('preds_haspeedee_test.xlsx')"
      ],
      "metadata": {
        "id": "TbfF-fxvWx2I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "d9qFolBSNBp9",
        "outputId": "f3c512b6-63f1-430f-f952-3b8dee0d0faa"
      },
      "execution_count": null,
      "outputs": [
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              "     anonymized_tweet_id                                    anonymized_text  \\\n",
              "0        172580609652325  La #Lega e il #m5s stanno dando a bere all'opi...   \n",
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              "..                   ...                                                ...   \n",
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              "\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = hs['label'].to_numpy()\n",
        "y_preds = hs['preds'].to_numpy()\n",
        "print(sklearn.metrics.classification_report(y_true, y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "359GiDDVW4wT",
        "outputId": "ebc9f595-747a-4a44-9be6-0c9caeab11ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.92      0.93       346\n",
            "           1       0.88      0.90      0.89       214\n",
            "\n",
            "    accuracy                           0.91       560\n",
            "   macro avg       0.91      0.91      0.91       560\n",
            "weighted avg       0.91      0.91      0.91       560\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M95ip0lONEiA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Augmentation"
      ],
      "metadata": {
        "id": "Jo9vG-7mNq2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_excel('haspeedee_train.xlsx')"
      ],
      "metadata": {
        "id": "U9i1od8iNr_4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.label.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwTXICnXNype",
        "outputId": "fdcdb78e-a4b5-49c5-b0a2-28c1f2989540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3110\n",
              "1    1930\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import imblearn"
      ],
      "metadata": {
        "id": "xX3JZPNSYkGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# summarize class distribution\n",
        "counter = Counter(y)\n",
        "print(counter)"
      ],
      "metadata": {
        "id": "zV_f_vREYwno"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pos = data[data['label'] == 1]"
      ],
      "metadata": {
        "id": "2aB09bE9b_uB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_pos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "myF0tVgxc0PR",
        "outputId": "e5473b90-ce4a-498a-e22e-f1812dbe238a"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      anonymized_tweet_id                                    anonymized_text  \\\n",
              "0         217874450618134  @unknown Con tutte le denunce che si sta becca...   \n",
              "2         817917484817935  Il #m5s ha votato contro le #unionicivili addu...   \n",
              "7         170378636015491  Ma se la #carfagna e #crosetto sono più a #sin...   \n",
              "12        906842219275344  Nemmeno la tradizionale conferenza stampa di f...   \n",
              "20        919547819000519  “#Bari: Con una ruspa rapinano un furgone port...   \n",
              "...                   ...                                                ...   \n",
              "5010      252759665270019  #Lega  e #salvinisciacallo senza #VERGOGNA e s...   \n",
              "5011      215800859616571  Queste sono le vaccate che solo il QI basso de...   \n",
              "5012      535723231606582  Sfiducia. Speranza: “Il nemico è il virus, non...   \n",
              "5013      518258878031869  Ah....\\nMi ricorda i collage che facevo quando...   \n",
              "5038      887061502405620  @401560935985106 @507718538254873 @28429789767...   \n",
              "\n",
              "      label            dataset data_type  \n",
              "0         1  training_politics     train  \n",
              "2         1  training_politics     train  \n",
              "7         1  training_politics     train  \n",
              "12        1  training_politics     train  \n",
              "20        1  training_politics     train  \n",
              "...     ...                ...       ...  \n",
              "5010      1  training_politics     train  \n",
              "5011      1  training_politics     train  \n",
              "5012      1  training_politics     train  \n",
              "5013      1  training_politics     train  \n",
              "5038      1  training_politics     train  \n",
              "\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5010</th>\n",
              "      <td>252759665270019</td>\n",
              "      <td>#Lega  e #salvinisciacallo senza #VERGOGNA e s...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5011</th>\n",
              "      <td>215800859616571</td>\n",
              "      <td>Queste sono le vaccate che solo il QI basso de...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5012</th>\n",
              "      <td>535723231606582</td>\n",
              "      <td>Sfiducia. Speranza: “Il nemico è il virus, non...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5013</th>\n",
              "      <td>518258878031869</td>\n",
              "      <td>Ah....\\nMi ricorda i collage che facevo quando...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5038</th>\n",
              "      <td>887061502405620</td>\n",
              "      <td>@401560935985106 @507718538254873 @28429789767...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1930 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f295554c-6426-4983-bef9-c9b630f99d5f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-f295554c-6426-4983-bef9-c9b630f99d5f 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-f295554c-6426-4983-bef9-c9b630f99d5f');\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": "markdown",
      "source": [
        "#Error Analysis"
      ],
      "metadata": {
        "id": "9BPq5W9DUODR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Error analysis\n",
        "error = hs[hs['label'] != hs['preds']]\n",
        "error.label.value_counts() #num di FP e FN è bilanciato, + 6 FP"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LS9Nu8M1ez3_",
        "outputId": "74bfa814-2993-45dc-a518-3cc38247fc17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    27\n",
              "1    21\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "text_list = error['anonymized_text'].tolist()\n",
        "#remove stopwords\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "\n",
        "stopwords = set(nltk.corpus.stopwords.words('italian'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2dfpscXfY1z",
        "outputId": "0a4001cc-9c92-4003-df0f-0217db7eb937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def convert(lst):\n",
        "    return ' '.join(lst).split()\n",
        "     \n",
        "print(convert(text_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmmDHlZpKoVU",
        "outputId": "71f8a7c1-39d0-4335-ba23-63294965f7a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['\"Paolo', 'Polidori\"', 'dovrebbe', 'dimettersi', 'per', 'manifesta', 'incapacità:', 'invece', 'di', 'provvedere', 'nei', 'modi', 'consentiti', 'dalla', '#legge', 'e', 'dalla', 'sua', 'funzione', 'di', '#vicesindaco', 'risolvendo', 'la', 'situazione', 'di', 'disagio,', 'ha', 'preferito', 'agire', 'da', 'vandalo.', 'Non', 'merita', 'di', 'stare', 'in', 'un', 'Consiglio', 'Comunale.', '#Trieste', '@868843245148057', 'W', 'il', '#m5s', 'No', 'alla', '#casta', 'dei', 'partiti.', 'No', 'al', '#malaffare', '@861285225934020', '#salvinidimettiti', 'e', '#salvinifattiprocessare', 'perché', 'siamo', 'tutti', 'uguali', 'davanti', 'alla', 'legge', 'e', 'la', '#Magistratura', 'è', 'ancora', 'indipendente.', 'Perché', '#siamoumani', 'e', 'basta', '#Diciotti', '#facciamorete', 'Se', 'la', '#SeaWatch', 'fosse', 'piena', 'di', 'gattini', 'sareste', 'già', 'partiti', 'con', 'i', 'pedalò', 'per', 'salvarli.', 'Mi', 'disgustate.', '#facciamorete', '#PortiAperti', '#salvinidimettiti', '#restiamoumani', 'Intanto', 'in', '#Spagna', 'il', 'governo', 'socialista', 'di', '#Sanchez', 'è', \"sull'orlo\", 'della', 'crisi,', 'stretto', 'nella', 'morsa', 'incrociata', 'di', 'indipendentisti', 'catalani', 'e', 'centro', 'destre:', 'bocciata', 'la', 'finanziaria,', 'si', 'va', 'verso', 'elezioni', 'anticipate.', 'Ecco', 'la', 'fine', 'dei', 'veri', '#burattini.', '#conte', '#Verhofstadt', 'Sono', 'iscritto', 'al', '#M5S', 'dal', '2010', 'e', 'voterò', 'NO', 'per', \"l'autorizzazione\", 'a', 'procedere,', 'non', 'voglio', 'più', 'vedere', 'nuovamente', \"l'italia\", 'in', 'mano', 'al', 'PD,', 'Berlusconi,', 'e', 'altre', 'bande', 'di', 'criminali', '#Diciotti', '#Saviano', '#17Febbraio', '#ottoemezzo', '#mezzorainpiu', '#m5s', '#BuonaDomenica', '#nonelarena', '#StaseraItalia', '#Rami', 'eh', 'niente', 'c’è', 'un', '#Ministro', 'che', 'non', 'sta', 'bene', ',', 'il', 'coraggio', 'è', 'fuoco,', 'e', 'il', 'bullismo', 'è', 'fumo.', '(Benjamin', 'Disraeli)', '#SalviniNonMiRappresenti', '#salvinidimettiti', 'Se', 'credete', 'che', 'la', '#legge', 'sul', '#RevengePorn', 'sia', 'utile', 'ed', 'efficace,', 'domandatevi', 'per', 'quale', 'motivo', 'non', 'fare', 'la', 'stessa', 'legge', 'per', 'proteggere', 'il', 'diritto', \"d'autore?\", 'Ah,', \"c'è\", 'già?', 'Immagino', 'quindi', 'che', 'il', 'problema', 'sia', 'risolto?', 'O', 'no?', 'La', 'fuffa', 'colpisce', 'anche', 'il', '#governo', 'dei', '#5stelle', 'Quelli', 'che', 'si', 'lamentano', 'della', 'puzza', 'di', 'urina', 'per', 'le', 'strade', 'di', '#Roma', 'sono', 'gli', 'stessi', 'che', 'dicono', '#Fateliscendere', 'o', '#portiaperti', '!', 'Ma', 'secondo', 'loro', 'chi', 'cazzo', 'è', 'che', 'piscia', 'per', 'strada', 'a', 'tutte', 'le', 'ore,', 'che', 'vagano', 'ubriachi', 'o', 'senza', 'meta?', 'Gli', 'alieni?', '#raggi', '#salvini', '#facciamorete', '@943206476637778', 'I', 'processi', 'si', 'fanno', 'in', 'tribunale,', 'smettiamola', 'di', 'strumentalizzare', 'i', 'bambini', '#salvinivergognati', 'Sta’', 'critica', 'di', '#salvini', 'per', 'abuso', 'di', 'moto', 'ad', 'acqua,', 'sarà', 'fondamentale', 'per', 'il', 'futuro', 'del', 'governo.', 'Ma', 'soprattutto', 'per', 'lo', 'spessore', 'degli', 'argomenti', 'delle', 'opposizioni.', 'C’è', 'il', 'tanto', 'per', 'diventare', 'salviniana', 'anche', 'se', 'non', 'lo', 'eri', 'mai', 'stata.', '#mortaccivostri', 'Piove', 'e', 'non', 'possiamo', 'nemmeno', 'dire', '#governoladro', '.', '#crisidigoverno', '#m5spd', '#PDM5S', '#governogiallorosso', '#GovernoConte2', '#Conte2', '#Mattarella', '#consultazionibis', '#7Ottobre', 'si', 'vota', 'per', 'il', 'taglio', 'dei', 'parlamentari....', 'Il', '#M5S', 'va', 'avanti', 'come', 'un', 'treno', 'per', 'mantenere', 'le', 'promesse', 'fatte.', 'Vediamo', 'cosa', 'farà', '#Renzi,', 'noi', 'li', 'mettiamo', 'alla', 'prova', 'e', 'li', 'smascheriamo', 'come', 'abbiamo', 'fatto', 'con', '#salvinitraditore.', 'W', '@761724759091727', '@507718538254873', '@815952505303105', '@531705355117684', 'I', '#rifiuti', 'sono', 'in', 'mano', 'alle', '#mafie.', 'Creano', 'il', 'problema', 'x', 'poi', 'essere', 'chiamati', 'a', 'risolverlo...', 'Lo', 'sanno', 'tutti', 'ma', 'non', 'si', 'fa', 'nulla', 'xchè', 'è', 'la', 'burocrazia', 'italiana', 'che', 'protegge', 'questi', 'delinquenti.', '#invasione', '#Roma', '#sinistra', '#conte', '#rifiuti', '#Spadafora:\"#DiMaio', 'ha', 'portato', '#m5s', 'al', '34%\".', 'No.', 'Lo', 'sta', 'portando', \"all'estinzione.\", 'Ha', 'ricevuto', 'in', 'eredità', 'un', 'capitale', 'accumulato', 'in', 'un', 'decennio', 'da', '#Grillo', 'e', '#Casaleggio', 'senior.', 'In', 'un', 'anno', 'e', 'mezzo', 'è', 'riuscito', 'a', 'dilapidarlo.', 'A', 'ogni', 'elezione', 'dimezzando', 'il', 'voto', 'precedente.#Cassamortaro', 'Oggi', 'sono', 'in', 'vena', 'di', 'sondaggi..', 'Chi', 'avrà', 'la', 'meglio', 'in', 'questo', 'scontro', 'figurato?', '#SardinecontroSalvini', '#Sardine', '#sardina', '#salviniscappa', '#gattini', '#gattinicontrosalvini', '#gattiniconSalvini', '#ItaliaViva', '#Lega', '#Pd', '#M5S', '#Renzi', '#Salvini', '#Zingaretti', '#DiMaio', 'Senza', 'la', '#Lezzi', 'non', 'avremmo', 'mai', 'saputo', 'cosa', 'stava', 'per', 'combinare', 'la', '#Bellanova,', 'del', 'partito', 'marcio', 'di', 'Renzi,', 'con', 'i', 'soldi', 'per', 'la', '#Xylella.', 'A', 'questo', 'serve', 'il', '#m5s,', 'anche', 'se', 'non', 'dovessero', 'governare,', 'rompono', 'le', 'balle.', 'Ricordatelo', 'alle', '#Regionali.', '#BuonNatale', '#25dicembre', '#conte', 'Una', 'politica', 'che', 'promette', 'di', 'togliere', 'la', 'legge', 'sull’#omofobia', 'in', 'Emilia', 'Romagna,', 'nell’eventualità', 'dovesse', 'vincere,', 'è', 'da', 'vile.', 'Quella', '#legge', 'dovrebbe', 'essere', 'valida', 'su', 'scala', 'nazionale', 'e', 'quella', 'troglodita', 'di', '#Meloni', 'dovrebbe', 'stare', 'zitta', 'almeno', 'su', 'queste', 'tematiche.', 'Che', 'madre!', 'Da', '@943206476637778', 'parole', 'indegne', 'sulle', '#donne', 'che', 'intaserebbero', 'i', 'pronto', 'soccorso', 'per', 'abortire', 'a', 'seguito', 'di', '\"stili', 'di', 'vita', 'incivili\".', 'Non', 'conosce', 'neppure', 'la', '#legge194.', 'Vergogna!', 'In', 'pratica', '#Bonino', 'chiede', 'a', '#contebugiardo', 'di', 'mentire', 'con', 'magiore', 'convinzione', 'e', 'spudoratezza,', 'o', 'il', 'progetto', 'unionista', 'crollerà.', 'Eh', 'cara,', \"c'hanno\", 'mica', 'tutti', 'il', 'fegato', 'tuo...', 'Salvini', 'che', '\"screma\"', 'i', 'commenti', 'negativi', 'bloccando...significa', 'che', \"c'è\", 'un', 'calo', 'ed', 'inesorabile', 'calo', 'dei', 'consensi...', 'Tutto', 'questo', 'è', 'bellissimo.', '#salvinicensuraitaliani', '#salviniblocca', '#salviniseiallafrutta', '#salvinivergognati', '#salvinicheraccontabufale', 'Ve', 'lo', 'ripeto,', 'cari', 'bambascioni', 'comunisti,', 'bloccare', 'non', 'è', 'censura', 'ma', 'un', 'legittimo', 'diritto', 'individuale.', 'Invece', 'segnalare', '(che', 'è', 'il', 'vostro', 'sport', 'preferito)', 'è', 'censura', 'di', 'stampo', 'sovietico.', '#salvinicensuraitaliani', '#Renzi', 'è', 'come', '#Totti', 'o', '#Delpiero', ',', 'messo', 'da', 'parte', 'troppo', 'presto', 'x', 'vari', 'motivi', 'dai', 'sinistri', 'incapaci,', 'non', 'si', 'può', 'fare', 'a', 'meno', 'del', 'suo', 'carattere', 'decisionale,', 'si', 'ama', 'o', 'si', 'odia', 'è', 'un', 'fuoriclasse', 'di', 'idee', 'e', 'competenza,la', 'sx', 'e', 'i', 'Grulli', 'non', 'si', 'accuseranno', 'mai', 'di', 'essere', '#indecenti', '#PD', '#m5s', '#DPCM:', 'le', 'mostre', 'si,', 'le', 'Messe', 'no.', 'La', 'Cei', 'emana', 'un', 'comunicato', 'di', 'durezza', 'spaventosa:', '\"è', 'compromessa', 'la', 'libertà', 'di', 'culto\".', 'Allora', 'il', 'Governo', 'annuncia', 'un', 'protocollo', 'per', '\"Messe', 'in', 'sicurezza\".', '1', 'passo', 'avanti', 'e', '2', 'indietro;', 'praticamente', \"l'emergenza\", 'gestita', 'come', 'il', 'Gioco', \"dell'Oca...\", 'Sono', 'ore', 'in', 'cui', 'onorevoli', 'della', '#Repubblica', 'da', '#Bagnai', 'a', '#Maloni', 'incitano', 'la', 'pancia', 'del', 'web', 'a', 'una', 'rivolta', 'contro', 'i', '#DPCM', 'a', 'tutela', 'dei', 'cittadini.', 'Con', 'frasi', 'come', '“dittatura', 'della', 'scienza”', 'e', '“', 'servi', 'della', 'imperialismo', 'tedesco”,', 'creano', 'un', 'mix', 'populista', 'esplosivo', 'teso', 'a', 'creare', 'il', 'caos', 'bloccare', 'tutti', 'quelli', 'che', 'critica', 'è', 'come', 'fare', 'entrare', 'solo', 'tifosi', 'di', 'una', 'squadra', 'in', 'stadio,', 'alla', 'fine', 'se', 'tu', 'gioca', 'male', 'fischi', 'arriverà', 'comunque', '#salvinivergognati', 'In', '#senato', 'si', 'sta', 'votando', 'ORA', 'una', 'vera', 'e', 'propria', 'SANATORIA', 'dei', 'MISFATTI', 'del', '#Governo', '#Conte.', 'Chiedono', 'la', 'conversione', 'in', '#Legge', 'del', '#decreto', 'del', '#25marzo.', 'In', 'parole', 'povere,', 'VOGLIONO', 'PARARSI', 'IL', 'SEDERE', 'SAPENDO', \"D'AVER\", 'AGITO', 'SENZA', 'ALCUNA', 'LEGITTIMAZIONE.', '@610058340653592', '#20maggio', 'Da', 'un', 'lato', 'i', '#dpcm.', 'Dall’altro', 'il', '#togaparty.', 'Mi', 'ricordate', 'chi', 'sarebbe', 'il', 'vero', 'pericolo', 'per', 'la', 'democrazia', 'in', 'Italia?', 'Che', 'mi', 'sono', 'distratto', 'un', 'attimo...ah', 'che', 'spasso', 'gli', 'intellettualò', 'de', 'noantri', '@804831429960458', '@833107889211981', '@804831429960458', 'togli', 'il', 'cancro', 'che', 'hai', 'con', 'te', '(', '#Renzi', '#ItaliaViva),', 'staccarlo', 'e', 'allontanalo', 'più', 'possibile', 'se', 'vuoi', 'veramente', 'il', 'bene', \"dell'Italia.\", 'SBRIGATI,', 'PERCHÉ', 'SONO', 'PRONTI', 'A', 'PUGNALARTI', 'DI', 'NUOVO.', 'La', '#Consulta', 'boccia', 'il', 'ricorso', 'di', '#Sgarbi', 'contro', 'i', '#Dpcm', 'di', '#Conte', '[URL]', 'via', '@436926648293277', 'sempre', 'più', 'caprone', '#italia', 'Ho', '#contributi', 'versati', 'per', 'soli', '18', 'anni.', 'E', 'quindi', 'non', 'avrò', '#diritto', 'alla', '#pensione.', 'Invece', 'un', '#parlamentare', 'con', 'solo', '5', 'anni', 'di', 'legislatura', \"l'avrà.\", 'Perché', '?', 'Senza', 'parlare', 'dei', '#vitalizi', 'ai', '#pregiudicati,', 'uno', 'schifo', 'che', 'grida', 'vendetta.', '#Riaprite', '#Aperture', '#Speranza', '#IoApro', 'aprite', 'tutto', 'dai', 'Matrimoni', 'a', 'gli', 'stadi', 'alle', 'semplici', 'partite', 'di', 'Calcetto', 'riaprite', 'tutto', 'e', 'basta,', 'e', 'levate', 'il', 'coprifuoco', 'che', 'ha', 'rotto', 'le', 'palle!', '#Draghi', '#iussoli', 'fermo', 'in', 'parlamento.', '#iusculturae', 'idem.', '#LeggeZan', 'bloccata', 'in', 'commissione.', 'Vitalizio', 'a', '#Formigoni', 'approvato', 'in', '2', 'minuti.', 'Non', 'è', 'vero', 'che', 'sono', 'lenti,', 'è', 'che', 'sono', 'proprio', 'stronzi', '#Sgarbi.', 'Le', 'giravolte', 'tragicomiche', 'del', '#ministro', 'della', 'malattia', '#Speranza!', '[URL]', 'via', '@919153421214213', '@247280650884591', '@299489797721037', 'Proprio', 'niente', 'coprifuoco,', 'queste', 'misure', 'da', 'regime', 'devono', 'terminare...basta', 'con', 'lo', 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    {
      "cell_type": "code",
      "source": [
        "split_text = convert(text_list)\n",
        "allWordExceptStopDist = nltk.FreqDist(w.lower() for w in split_text if w not in stopwords) "
      ],
      "metadata": {
        "id": "tmDZcC-Gh0BG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mostCommon= allWordExceptStopDist.most_common(120)\n",
        "mostCommon"
      ],
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        },
        "id": "nPvpPr9EAaiM",
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    {
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              "     anonymized_tweet_id                                    anonymized_text  \\\n",
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              "63       624468506805769  @943206476637778 I processi si fanno in tribun...   \n",
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              "100      996012725659669  #7Ottobre si vota per il taglio dei parlamenta...   \n",
              "110      790286679429588  @531705355117684 I #rifiuti sono in mano alle ...   \n",
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              "207      204466817559986  Sono ore in cui onorevoli della #Repubblica da...   \n",
              "214      460699352300164  bloccare tutti quelli che critica è come fare ...   \n",
              "227      102713507179810  In #senato si sta votando ORA una vera e propr...   \n",
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              "271       14016802699497  @804831429960458 @833107889211981 @80483142996...   \n",
              "273      427064705745262  La #Consulta  boccia il ricorso di #Sgarbi con...   \n",
              "278      732028699810767  Ho #contributi versati per soli  18 anni. E qu...   \n",
              "290      210275312239157  #Riaprite #Aperture #Speranza #IoApro aprite t...   \n",
              "294       26608328892644  #iussoli fermo in parlamento.\\n#iusculturae id...   \n",
              "336       80461931930531  #Sgarbi. Le giravolte tragicomiche del #minist...   \n",
              "370       19088666328293  @247280650884591 @299489797721037 Proprio nien...   \n",
              "377      607985140241800  @25794416770063 La pentacoglionizzazione del @...   \n",
              "385       77564359597869  Facciamo facile ironia sul governo dei miglior...   \n",
              "386      271841305458904                        Sempre #salvinimerda✊ [URL]   \n",
              "401      177036746522656  La linea Salvini ha prevalso su Speranza che e...   \n",
              "419      243644964711753  INTERE  NAZIONI C HE  SONO  GIA'  AVANTI  RISP...   \n",
              "466      931524551145932  #Gelmini : assicura che se sfori il #Coprifuoc...   \n",
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              "501       22381323525867  @466272085376114 @804831429960458 No no, le pr...   \n",
              "517      559624897727552  Neanche un poco.#1,9 . #renzi statista incompr...   \n",
              "533      178155546851705  @465893524075273 @221081711272552 Solo una per...   \n",
              "\n",
              "     label            dataset data_type  preds  \n",
              "2        1  training_politics       val      0  \n",
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              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>229647197643357</td>\n",
              "      <td>Intanto in #Spagna il governo socialista di #S...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>733879466972060</td>\n",
              "      <td>Sono iscritto al #M5S dal 2010 e voterò NO per...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>71184870630357</td>\n",
              "      <td>#Rami eh niente c’è un #Ministro che non sta b...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>820433310798508</td>\n",
              "      <td>Se credete che la #legge sul #RevengePorn sia ...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>494786086468365</td>\n",
              "      <td>Quelli che si lamentano della puzza di urina p...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>624468506805769</td>\n",
              "      <td>@943206476637778 I processi si fanno in tribun...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>488874347250936</td>\n",
              "      <td>Sta’ critica di #salvini per abuso di moto ad ...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>686391830263793</td>\n",
              "      <td>Piove e non possiamo nemmeno dire #governoladr...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>996012725659669</td>\n",
              "      <td>#7Ottobre si vota per il taglio dei parlamenta...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>790286679429588</td>\n",
              "      <td>@531705355117684 I #rifiuti sono in mano alle ...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>455802022502279</td>\n",
              "      <td>#Spadafora:\"#DiMaio ha portato #m5s al 34%\".\\n...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>587591403209253</td>\n",
              "      <td>Oggi sono in vena di sondaggi..\\nChi avrà la m...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>324762057642948</td>\n",
              "      <td>Senza la #Lezzi non avremmo mai saputo cosa st...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>671863277559893</td>\n",
              "      <td>Una politica che promette di togliere la legge...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>566202981170350</td>\n",
              "      <td>Da @943206476637778 parole indegne sulle #donn...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>498651420615356</td>\n",
              "      <td>In pratica #Bonino chiede a #contebugiardo di ...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>826767637073989</td>\n",
              "      <td>Salvini che \"screma\" i commenti negativi blocc...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>931442650581126</td>\n",
              "      <td>Ve lo ripeto, cari bambascioni comunisti, bloc...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>486078503397400</td>\n",
              "      <td>#Renzi è come #Totti o #Delpiero , messo da pa...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>271990512864934</td>\n",
              "      <td>#DPCM: le mostre si, le Messe no. La Cei emana...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>204466817559986</td>\n",
              "      <td>Sono ore in cui onorevoli della #Repubblica da...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>460699352300164</td>\n",
              "      <td>bloccare tutti quelli che critica è come fare ...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>102713507179810</td>\n",
              "      <td>In #senato si sta votando ORA una vera e propr...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>753504517982026</td>\n",
              "      <td>Da un lato i #dpcm. Dall’altro il #togaparty. ...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>14016802699497</td>\n",
              "      <td>@804831429960458 @833107889211981 @80483142996...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>427064705745262</td>\n",
              "      <td>La #Consulta  boccia il ricorso di #Sgarbi con...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>732028699810767</td>\n",
              "      <td>Ho #contributi versati per soli  18 anni. E qu...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>210275312239157</td>\n",
              "      <td>#Riaprite #Aperture #Speranza #IoApro aprite t...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>26608328892644</td>\n",
              "      <td>#iussoli fermo in parlamento.\\n#iusculturae id...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>80461931930531</td>\n",
              "      <td>#Sgarbi. Le giravolte tragicomiche del #minist...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370</th>\n",
              "      <td>19088666328293</td>\n",
              "      <td>@247280650884591 @299489797721037 Proprio nien...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>377</th>\n",
              "      <td>607985140241800</td>\n",
              "      <td>@25794416770063 La pentacoglionizzazione del @...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>385</th>\n",
              "      <td>77564359597869</td>\n",
              "      <td>Facciamo facile ironia sul governo dei miglior...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>271841305458904</td>\n",
              "      <td>Sempre #salvinimerda✊ [URL]</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401</th>\n",
              "      <td>177036746522656</td>\n",
              "      <td>La linea Salvini ha prevalso su Speranza che e...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>243644964711753</td>\n",
              "      <td>INTERE  NAZIONI C HE  SONO  GIA'  AVANTI  RISP...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466</th>\n",
              "      <td>931524551145932</td>\n",
              "      <td>#Gelmini : assicura che se sfori il #Coprifuoc...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>468</th>\n",
              "      <td>65525355900148</td>\n",
              "      <td>Berlusconi ancora in ospedale in occasione di ...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>763900047877119</td>\n",
              "      <td>#Letta provocarà #Salvini, sempre di più, perc...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>428977403543317</td>\n",
              "      <td>@943206476637778 Continuate così, la #lega è u...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490</th>\n",
              "      <td>641771643072500</td>\n",
              "      <td>#Draghi\\nFavole e bugie dei mitomani di #gover...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>22381323525867</td>\n",
              "      <td>@466272085376114 @804831429960458 No no, le pr...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>517</th>\n",
              "      <td>559624897727552</td>\n",
              "      <td>Neanche un poco.#1,9 . #renzi statista incompr...</td>\n",
              "      <td>0</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>533</th>\n",
              "      <td>178155546851705</td>\n",
              "      <td>@465893524075273 @221081711272552 Solo una per...</td>\n",
              "      <td>1</td>\n",
              "      <td>training_politics</td>\n",
              "      <td>val</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c525d598-496c-40b2-ac01-bc2246d7f0eb')\"\n",
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          },
          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "Patterns di errori: \n",
        "\n",
        "FALSE POSITIVES:\n",
        "\n",
        "1) Frasi contenenti sentiment negativo ma verso statements razzisti (Se la #SeaWatch fosse piena di gattini sareste già partiti con i pedalò per salvarli. *Mi disgustate.* #facciamorete #PortiAperti #salvinidimettiti #restiamoumani)\n",
        "\n",
        "FALSE NEGATIVES:\n",
        "\n",
        "1) Soggetto implicito (Quelli che si lamentano della puzza di urina per le strade di #Roma sono gli stessi che dicono #Fateliscendere o #portiaperti ! Ma secondo loro chi cazzo è che *piscia per strada a tutte le ore, che vagano ubriachi o senza meta?* Gli alieni? #raggi #salvini #facciamorete)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sbDoyQ8oiOHn"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eKN6X4feZ4j0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}