Result.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUhGFH3PRMKz"
      },
      "source": [
        "### BERT fine-tuning for document classification"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Global Variables"
      ],
      "metadata": {
        "id": "IyNZsLj-Gsdf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "d28sFueR3U9k"
      },
      "outputs": [],
      "source": [
        "MODEL=\"bert-base-uncased\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ker_mgZI8WP"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ag2obpM48IRq",
        "outputId": "c3044da4-3fc6-4ee0-ccf8-a10fef6bff2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully installed pyyaml-6.0 sacremoses-0.0.49 tokenizers-0.12.1 transformers-4.18.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### All Imports"
      ],
      "metadata": {
        "id": "Hm8RxubNFasl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "T7xim5QEF5vd"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "import os\n",
        "import re\n",
        "import numpy as np \n",
        "from sklearn.metrics import accuracy_score\n",
        "import transformers\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "from transformers import AutoTokenizer\n",
        "import torch\n",
        "from torch import cuda\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "from transformers import AutoTokenizer\n",
        "import numpy as np\n",
        "from scipy.special import softmax\n",
        "import csv\n",
        "import urllib.request\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FGuxfTEFurq"
      },
      "source": [
        "### Emotion Dataset from Huggingface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
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          ]
        },
        "id": "autb5_cl2zY3",
        "outputId": "0ad4d318-e3a7-46ac-ce75-d461beab3955"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using custom data configuration default\n",
            "Reusing dataset emotion (/root/.cache/huggingface/datasets/emotion/default/0.0.0/348f63ca8e27b3713b6c04d723efe6d824a56fb3d1449794716c0f0296072705)\n"
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        },
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            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dataset=load_dataset('emotion')\n",
        "#dataset = load_dataset(\"sem_eval_2018_task_1\",'subtask5.english')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3mbI_KSA6gI"
      },
      "source": [
        "### Twitter Dataset for our Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIXH7P8dBDfu",
        "outputId": "fd5d8d0d-1079-4b2a-c93a-0f3f9858cb43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import pandas as pd\n",
        "dataset=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/covid19_tweets.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "yhZYMwRhBTZC"
      },
      "outputs": [],
      "source": [
        "X = dataset['text'].values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6-gz2ixF8zc"
      },
      "source": [
        "### TweetEval Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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      "outputs": [
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            "Downloading and preparing dataset tweet_eval/emotion (download: 472.47 KiB, generated: 511.52 KiB, post-processed: Unknown size, total: 984.00 KiB) to /root/.cache/huggingface/datasets/tweet_eval/emotion/1.1.0/12aee5282b8784f3e95459466db4cdf45c6bf49719c25cdb0743d71ed0410343...\n"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/374 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f3f5aa5aecd4d68ba8378f4242cc798"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset tweet_eval downloaded and prepared to /root/.cache/huggingface/datasets/tweet_eval/emotion/1.1.0/12aee5282b8784f3e95459466db4cdf45c6bf49719c25cdb0743d71ed0410343. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1450bdd3cb8480aa7f32996d1d86fe2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "task='emotion'\n",
        "dataset=load_dataset('tweet_eval',task)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "G6RGUlNVhP-e"
      },
      "outputs": [],
      "source": [
        "labels=[]\n",
        "mapping_link = f\"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt\"\n",
        "with urllib.request.urlopen(mapping_link) as f:\n",
        "    html = f.read().decode('utf-8').split(\"\\n\")\n",
        "    csvreader = csv.reader(html, delimiter='\\t')\n",
        "labels = [row[1] for row in csvreader if len(row) > 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "10054882"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "train_data=pd.DataFrame(dataset['train'])\n",
        "test_data=pd.DataFrame(dataset['test'])\n",
        "#val_data=pd.DataFrame(dataset['validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "od6diAMhbFwp"
      },
      "outputs": [],
      "source": [
        "train_X=train_data['text'].values\n",
        "len(train_X)\n",
        "train_labels=train_data['label'].values\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "ohe = preprocessing.OneHotEncoder()\n",
        "le.fit(train_labels) \n",
        "y_train = le.transform(train_labels).reshape(-1, 1) # this is magic\n",
        "ohe.fit(y_train)\n",
        "train_y = ohe.transform(y_train).todense()\n",
        "del y_train\n",
        "del train_labels\n",
        "del train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "O0pr_9pU9Mbo"
      },
      "outputs": [],
      "source": [
        "test_X=test_data['text'].values\n",
        "test_labels=test_data['label'].values\n",
        "le = preprocessing.LabelEncoder()\n",
        "ohe = preprocessing.OneHotEncoder()\n",
        "le.fit(test_labels) \n",
        "y_test = le.transform(test_labels).reshape(-1, 1) # this is magic\n",
        "ohe.fit(y_test)\n",
        "test_y = ohe.transform(y_test).todense()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Q01C6h7V1U3j"
      },
      "outputs": [],
      "source": [
        "del y_test\n",
        "del test_labels\n",
        "del test_data\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lit6LeDFZu8q"
      },
      "source": [
        "## train data for semeval 2018 task 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZV67UBH-5sfi"
      },
      "outputs": [],
      "source": [
        "#train_X=train_data['Tweet'] \n",
        "train_y=train_data.drop(columns=['ID','Tweet'])\n",
        "train_X=dataset['train']['Tweet']\n",
        "del train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aanjh66iH6yg"
      },
      "outputs": [],
      "source": [
        "train_y=train_y.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaajOl4W5v8j"
      },
      "outputs": [],
      "source": [
        "\n",
        "#test_X=test_data['Tweet']\n",
        "test_y=test_data.drop(columns=['ID','Tweet'])\n",
        "test_X=dataset['test']['Tweet']\n",
        "del test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFJJtdUoH5zi"
      },
      "outputs": [],
      "source": [
        "test_y=test_y.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVQKSVEJDMGw",
        "outputId": "8c23dff6-20ca-48b8-b5be-fd5ab91d11aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6838\n",
            "6838\n"
          ]
        }
      ],
      "source": [
        "print(len(train_X))\n",
        "print(len(train_y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-U2zVZhH0U4"
      },
      "source": [
        "## Model Init"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ga-9fRrpIMbZ"
      },
      "source": [
        "### Run this cell for the first time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4LaHQhnlRMK5",
        "outputId": "5f654577-e3d9-422d-f83d-bc17884a8f58"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "device = 'cuda' if cuda.is_available() else 'cpu'\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awy3tUD2RMLI"
      },
      "source": [
        "### Torch Datasets\n",
        "\n",
        "- takes in inputs and outputs/labels\n",
        "- interfaces with tokenizer\n",
        "- handles batching"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "HYxVIYT8RMLJ"
      },
      "outputs": [],
      "source": [
        "def preprocess(text):\n",
        "    new_text = []\n",
        "    for t in text.split(\" \"):\n",
        "        t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
        "        t = 'http' if t.startswith('http') else t\n",
        "        new_text.append(t)\n",
        "    return \" \".join(new_text)\n",
        "\n",
        "class MultiLabelDataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, text, labels, tokenizer, max_len):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.text = text\n",
        "        self.targets = labels\n",
        "        self.max_len = max_len\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.text)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        text = self.text[index]\n",
        "        inputs = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            None,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            pad_to_max_length=True,\n",
        "            truncation=True,\n",
        "            return_token_type_ids=True\n",
        "        )\n",
        "        ids = inputs['input_ids']\n",
        "        mask = inputs['attention_mask']\n",
        "        token_type_ids = inputs[\"token_type_ids\"]\n",
        "\n",
        "\n",
        "        return {\n",
        "            'ids': torch.tensor(ids, dtype=torch.long),\n",
        "            'mask': torch.tensor(mask, dtype=torch.long),\n",
        "            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n",
        "            'targets': torch.tensor(self.targets[index], dtype=torch.long)\n",
        "        }\n",
        "        \n",
        "class NoLabelDataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, text, tokenizer, max_len):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.text = text\n",
        "        self.max_len = max_len\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.text)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        text = self.text[index]\n",
        "        inputs = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            None,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            pad_to_max_length=True,\n",
        "            truncation=True,\n",
        "            return_token_type_ids=True\n",
        "        )\n",
        "        ids = inputs['input_ids']\n",
        "        mask = inputs['attention_mask']\n",
        "        token_type_ids = inputs[\"token_type_ids\"]\n",
        "\n",
        "\n",
        "        return {\n",
        "            'ids': torch.tensor(ids, dtype=torch.long),\n",
        "            'mask': torch.tensor(mask, dtype=torch.long),\n",
        "            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long)\n",
        "        }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-m3w1JTzRMLK"
      },
      "source": [
        "### Bert Class\n",
        "\n",
        "- first \"layer\" is a pre-trained BERT model\n",
        "- you can add whatever layers you want after that"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "37qPTfHSRMLK"
      },
      "outputs": [],
      "source": [
        "class BERTClass(torch.nn.Module):\n",
        "    def __init__(self, NUM_OUT):\n",
        "        super(BERTClass, self).__init__()\n",
        "                   \n",
        "        self.l1 = BertModel.from_pretrained(MODEL)\n",
        "#         self.pre_classifier = torch.nn.Linear(768, 256)\n",
        "        self.classifier = torch.nn.Linear(768, NUM_OUT)\n",
        "#         self.dropout = torch.nn.Dropout(0.5)\n",
        "        self.softmax = torch.nn.Softmax(dim=1)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask, token_type_ids):\n",
        "        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        hidden_state = output_1[0]\n",
        "        pooler = hidden_state[:, 0]\n",
        "#         pooler = self.pre_classifier(pooler)\n",
        "#         pooler = torch.nn.Tanh()(pooler)\n",
        "#         pooler = self.dropout(pooler)\n",
        "        output = self.classifier(pooler)\n",
        "        output = self.softmax(output)\n",
        "        return output\n",
        "class AutoClass(torch.nn.Module):\n",
        "    def __init__(self, NUM_OUT):\n",
        "        super(AutoClass, self).__init__()\n",
        "                   \n",
        "        self.l1 = AutoModelForSequenceClassification.from_pretrained(MODEL)\n",
        "#         self.pre_classifier = torch.nn.Linear(768, 256)\n",
        "        self.classifier = torch.nn.Linear(768, NUM_OUT)\n",
        "#         self.dropout = torch.nn.Dropout(0.5)\n",
        "        self.softmax = torch.nn.Softmax(dim=1)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask, token_type_ids):\n",
        "        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        hidden_state = output_1[0]\n",
        "        pooler = hidden_state[:, 0]\n",
        "#         pooler = self.pre_classifier(pooler)\n",
        "#         pooler = torch.nn.Tanh()(pooler)\n",
        "#         pooler = self.dropout(pooler)\n",
        "        output = self.classifier(pooler)\n",
        "        output = self.softmax(output)\n",
        "        return output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXziYRKERMLL"
      },
      "source": [
        "### Helpful Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgq_pJK6M6-D"
      },
      "source": [
        "\n",
        "Loss\n",
        "\n",
        "- This task is binary, so it uses binary crossentropy loss\n",
        "- Tasks with more labels will use categorical crossentropy\n",
        "- Tasks that don't have labels, but rather have distributions should use KL divergence\n",
        "- Tasks that don't have distributions should use something like RMSE loss\n",
        "\n",
        "Train\n",
        "\n",
        "- Steps through the data batch by batch\n",
        "- grabs ids, masks, and token_type_ids which are required inputs for BERT\n",
        "- inputs are passed through the model, compared to targets, computes loss function, backprops\n",
        "\n",
        "Validation\n",
        "\n",
        "- Takes a model, passes inputs\n",
        "- Need to use the targets from here because they are potentially shuffled!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "KZCqrV_gRMLL"
      },
      "outputs": [],
      "source": [
        "def loss_fn(outputs, targets):\n",
        "    #print(outputs,targets)\n",
        "    return torch.nn.CrossEntropyLoss()(outputs, targets)\n",
        "\n",
        "def train(model, training_loader, optimizer):\n",
        "    model.train()\n",
        "    for data in tqdm(training_loader):\n",
        "        ids = data['ids'].to(device, dtype = torch.long)\n",
        "        mask = data['mask'].to(device, dtype = torch.long)\n",
        "        token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "        targets = data['targets'].to(device, dtype = torch.float)\n",
        "\n",
        "        outputs = model(ids, mask, token_type_ids)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = loss_fn(outputs, targets)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    return outputs\n",
        "    \n",
        "def validation(model, testing_loader):\n",
        "    model.eval()\n",
        "    fin_targets=[]\n",
        "    fin_outputs=[]\n",
        "    with torch.no_grad():\n",
        "        for data in tqdm(testing_loader):\n",
        "            targets = data['targets']\n",
        "            ids = data['ids'].to(device, dtype = torch.long)\n",
        "            mask = data['mask'].to(device, dtype = torch.long)\n",
        "            token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "            outputs = model(ids, mask, token_type_ids)\n",
        "            outputs = torch.sigmoid(outputs).cpu().detach()\n",
        "            fin_outputs.extend(outputs)\n",
        "            fin_targets.extend(targets)\n",
        "    return torch.stack(fin_outputs), torch.stack(fin_targets)\n",
        "def predict(model, testing_loader):\n",
        "    model.eval()\n",
        "    fin_outputs=[]\n",
        "    with torch.no_grad():\n",
        "        for data in tqdm(testing_loader):\n",
        "            #targets = data['targets']\n",
        "            ids = data['ids'].to(device, dtype = torch.long)\n",
        "            mask = data['mask'].to(device, dtype = torch.long)\n",
        "            token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "            outputs = model(ids, mask, token_type_ids)\n",
        "            outputs = torch.sigmoid(outputs).cpu().detach()\n",
        "            fin_outputs.extend(outputs)\n",
        "            #fin_targets.extend(targets)\n",
        "    return torch.stack(fin_outputs)#, torch.stack(fin_targets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYt6iGifRMLN"
      },
      "source": [
        "## Training\n",
        "\n",
        "- hyperparameters\n",
        "- setup dataset\n",
        "- setup parameters\n",
        "- setup dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558,
          "referenced_widgets": [
            "8b050f27358541efaaf9609592b9b380",
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            "8acbbd52bc3a451588cafb4e6ab1f889",
            "9d59db030aa544ea8bed61eb9b14f659",
            "8429f771882a4b69b10239ba264d2236",
            "1354e5f9f2a34852907c7b7df30ad95d",
            "f081d63434754a47a2d28aae510c95f1",
            "1f78b2cdd4294165b14f3225efffe470",
            "0f08e2be290843f78c0ca14a33107126",
            "c148e94ff5504a839528fd46f4fe0004",
            "2a95945ae3f747daaa726e5dcc9c5eb9",
            "f4e084a6a1a04151a0b96d398f3169a6",
            "607f9bca3c814599a99472ed02f098db",
            "d5e4e96304f54550b2df948dcdb011e6",
            "2821f6d723424739a447bd2aacbd2505",
            "d3cd58d4760e48f0bb9744daa74a4f67",
            "b8ab951d8009443a87ad84a6d827b1e9",
            "207eaae8e879420b86f823c726c9e787",
            "59d2f7a53ada4dbea3b2c32321b437fc",
            "4f6f4037f095471fa053bc7d2110ba4a"
          ]
        },
        "id": "HyJ0s9uMRMLO",
        "outputId": "1a8afcca-aa71-474a-8172-17a2331405a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b050f27358541efaaf9609592b9b380"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy on test set 0.7565095003518649\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/51 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28798144331e435a99a862c3b851280a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: 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",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1ffff3b9ce8468f8adb1f14575f845f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy on test set 0.757213230119634\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/51 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "44bdf95bf7d54281b1f28381a2bf9af5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: 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",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c148e94ff5504a839528fd46f4fe0004"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy on test set 0.7860661505981703\n"
          ]
        }
      ],
      "source": [
        "MAX_LEN = 128\n",
        "BATCH_SIZE = 64\n",
        "EPOCHS = 3\n",
        "NUM_OUT = 4 # binary task\n",
        "LEARNING_RATE = 2e-05\n",
        "i=0\n",
        "for txt in train_X:\n",
        "  train_X[i]=preprocess(txt)\n",
        "  i+=1\n",
        "tokenizer=BertTokenizer.from_pretrained(MODEL)\n",
        "training_data = MultiLabelDataset(train_X, torch.from_numpy(train_y), tokenizer, MAX_LEN)\n",
        "test_data = MultiLabelDataset(test_X, torch.from_numpy(test_y), tokenizer, MAX_LEN)\n",
        "\n",
        "train_params = {'batch_size': BATCH_SIZE,\n",
        "                'shuffle': True,\n",
        "                'num_workers': 0\n",
        "                }\n",
        "\n",
        "test_params = {'batch_size': BATCH_SIZE,\n",
        "                'shuffle': True,\n",
        "                'num_workers': 0\n",
        "                }    \n",
        "\n",
        "training_loader = torch.utils.data.DataLoader(training_data, **train_params)\n",
        "testing_loader = torch.utils.data.DataLoader(test_data, **test_params)\n",
        "model = BERTClass(NUM_OUT)\n",
        "model.to(device)    \n",
        "\n",
        "optimizer = torch.optim.Adam(params =  model.parameters(), lr=LEARNING_RATE)\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    outputs = train(model, training_loader, optimizer)\n",
        "    #print(f'Epoch: {epoch}, Loss:  {loss.item()}')  \n",
        "    guess, targs = validation(model, testing_loader)\n",
        "    guesses = torch.max(guess, dim=1)\n",
        "    targets = torch.max(targs, dim=1)\n",
        "    print('accuracy on test set {}'.format(accuracy_score(guesses.indices, targets.indices)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Free up Memory"
      ],
      "metadata": {
        "id": "JWINv4E4Hl1l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "del train_X\n",
        "del test_X\n",
        "del train_y\n",
        "del test_y\n",
        "del training_data\n",
        "del test_data\n",
        "del training_loader\n",
        "del testing_loader\n",
        "del guesses\n",
        "del targets"
      ],
      "metadata": {
        "id": "9k-bw5Z4HqB4"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmQjNaEYeZCU"
      },
      "source": [
        "## Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "HQMlmXNqYbiN"
      },
      "outputs": [],
      "source": [
        "trained_model=model\n",
        "del model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "eSQWaiQ2kTio"
      },
      "outputs": [],
      "source": [
        "tokenizer=BertTokenizer.from_pretrained(MODEL)\n",
        "real_test_data = NoLabelDataset(X, tokenizer, MAX_LEN)\n",
        "\n",
        "test_params = {'batch_size': BATCH_SIZE,\n",
        "                'shuffle': True,\n",
        "                'num_workers': 0\n",
        "                }    \n",
        "real_testing_loader = torch.utils.data.DataLoader(real_test_data, **test_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "163be8200b8c4908b2c93c5a0e4b98f8",
            "69d82f347be747a6b881e102a2a58b7a",
            "b0219af0d5bc49d8a0e07d89cc4d550a",
            "f28688ee2b9b4d8c9920c1ca79c5a167",
            "0f56dd6df68d494b8a01b92313e3613a",
            "31f7706e31a64e3b8d0176823e60aaeb",
            "181c211f957f45e8a81c96b48532a170",
            "99bfa9abd8af4e018e4b6d78e6184eba",
            "bca0e9c622634901870dbf64b8832f4a",
            "d4431ba777d0493c971157dc59974ff4",
            "f317a2cd50a34b4a839c2faed00a7ff0"
          ]
        },
        "id": "XSwBv-ifd4GV",
        "outputId": "c6213dde-4295-4b47-f3ab-984cedf3bd8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2799 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "163be8200b8c4908b2c93c5a0e4b98f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: 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",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "guess = predict(trained_model, real_testing_loader)\n",
        "#guesses = torch.max(guess, dim=1)\n",
        "#print('accuracy on test set {}'.format(accuracy_score(guesses.indices, targets.indices)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Appending Predicted Score to Dataset\n"
      ],
      "metadata": {
        "id": "YbLgcJpj4hQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emotion_score=[[]]\n",
        "for g in guess:\n",
        "  score = g.numpy()\n",
        "  emotion_score.append(softmax(score))"
      ],
      "metadata": {
        "id": "P1GRrvdhaCUW"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[['anger','joy','optimism','sadness']]=emotion_score[1:]"
      ],
      "metadata": {
        "id": "Bpm1CVBaaeIy"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.sort_values(by=['date'],inplace=True)"
      ],
      "metadata": {
        "id": "Zemc-j4ra_Mj"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[['user_location','emotion']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uaWbCN7PV3Ep",
        "outputId": "dd05caea-564b-4899-d579-520eebc86afe"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     user_location emotion\n",
              "0                       astroworld   anger\n",
              "1                     New York, NY   anger\n",
              "2                 Pewee Valley, KY   anger\n",
              "3             Stuck in the Middle      joy\n",
              "4                Jammu and Kashmir     joy\n",
              "...                            ...     ...\n",
              "179103             Ilorin, Nigeria   anger\n",
              "179104                     Ontario     joy\n",
              "179105                   🇨🇦 Canada   anger\n",
              "179106               New York City   anger\n",
              "179107  Aliwal North, South Africa     joy\n",
              "\n",
              "[179108 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dab3b6d8-58c6-4e77-b012-2c02e771fb81\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_location</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>astroworld</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New York, NY</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pewee Valley, KY</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stuck in the Middle</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jammu and Kashmir</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179103</th>\n",
              "      <td>Ilorin, Nigeria</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179104</th>\n",
              "      <td>Ontario</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179105</th>\n",
              "      <td>🇨🇦 Canada</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179106</th>\n",
              "      <td>New York City</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179107</th>\n",
              "      <td>Aliwal North, South Africa</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>179108 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dab3b6d8-58c6-4e77-b012-2c02e771fb81')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-dab3b6d8-58c6-4e77-b012-2c02e771fb81 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-dab3b6d8-58c6-4e77-b012-2c02e771fb81');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[['user_location']].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3ElZcDmWZtD",
        "outputId": "a6566770-a851-490b-e6d1-71ce02eda5fb"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_location         \n",
              "india                     3842\n",
              "united states             2457\n",
              "worldwide                 1738\n",
              "new delhi, india          1731\n",
              "mumbai, india             1406\n",
              "                          ... \n",
              "liverpool kulon              1\n",
              "liverpool in europe          1\n",
              "liverpool england            1\n",
              "liverpool city council       1\n",
              "lordshill southampton        1\n",
              "Length: 26036, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['user_location']=dataset['user_location'].str.lower()\n",
        "dataset[['loc1','loc2']]=dataset['user_location'].str.split(',',n=1, expand=True)\n"
      ],
      "metadata": {
        "id": "wjorWh8WXEVG"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[['loc2']].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73Mrf5CaXqTg",
        "outputId": "b968e17c-e617-4375-d0f0-6b4c5e3f25d2"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loc2           \n",
              " india             9095\n",
              " usa               5119\n",
              " england           3555\n",
              " ca                3332\n",
              " ny                2125\n",
              "                   ... \n",
              " on 🇨🇦                1\n",
              " fl ⚓️🏝🛳🏠🏢🌅⛴          1\n",
              " abuja, nigeria       1\n",
              " on, canada           1\n",
              "🇬🇧🇪🇺 for work         1\n",
              "Length: 4601, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask = (dataset['loc2'] == 'india') | (dataset['loc2'] =='usa')"
      ],
      "metadata": {
        "id": "Jj9-6tdtcHuQ"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "id": "5k5bQd4bdFXt",
        "outputId": "0a59e58b-d5b0-489d-b2f8-a97bdde811d9"
      },
      "execution_count": 132,
      "outputs": [
        {
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              "                         user_name               user_location  \\\n",
              "17175        Jessica Luther Rummel               denton, texas   \n",
              "17174   Dr. Lipi #TrustDrFauci Roy               new york, usa   \n",
              "17173                  Patty Hayes                 seattle, wa   \n",
              "17172                 Clive Gorman  victoria, british columbia   \n",
              "17171              Tristyn Russelo             alberta, canada   \n",
              "...                            ...                         ...   \n",
              "166660             Dirk Haussecker                   rna world   \n",
              "166659                Natalie B 🔶️                     england   \n",
              "166658   Keith Bishop 👨‍👨‍👧‍👦 🏳️‍🌈       newman university, uk   \n",
              "166657                 Kings Place         kings cross, london   \n",
              "166656                  🍺¯\\_(ツ)_/¯                  boston, ma   \n",
              "\n",
              "                                         user_description  \\\n",
              "17175   Mother. Partner. Scholar. Activist. Alchemist....   \n",
              "17174   @NBCNews @MSNBC @Forbes Medical Contributor | ...   \n",
              "17173   Director of Public Health - Seattle & King Cou...   \n",
              "17172   Marketing Director @CNEGames for @idlechampion...   \n",
              "17171                                                 NaN   \n",
              "...                                                   ...   \n",
              "166660  RNA(i) Therapeutics Investor-Scientist who lov...   \n",
              "166659  single mum. Lib Dem. outspoken on Freedom of s...   \n",
              "166658  Foster Dad, Youth Worker & Senior Lecturer (Ch...   \n",
              "166657  An open, multi-arts venue presenting an advent...   \n",
              "166656  Father, husband, musician, dog-lover. All opin...   \n",
              "\n",
              "               user_created  user_followers  user_friends  user_favourites  \\\n",
              "17175   2020-07-05 03:28:16             234            28              220   \n",
              "17174   2009-10-11 18:46:51           12485          4603            38120   \n",
              "17173   2017-07-07 18:56:50             718           162             2276   \n",
              "17172   2011-12-23 02:02:35             597           224            23824   \n",
              "17171   2017-06-14 22:01:54               5            68               57   \n",
              "...                     ...             ...           ...              ...   \n",
              "166660  2011-11-10 12:00:58            9341           339             7276   \n",
              "166659  2019-01-06 14:22:49             439           586              165   \n",
              "166658  2015-06-20 10:56:58            1244           667             1140   \n",
              "166657  2009-02-26 12:16:47           18930          6213            23986   \n",
              "166656  2009-04-13 13:14:46              24           197             1409   \n",
              "\n",
              "        user_verified                 date  \\\n",
              "17175           False  2020-07-24 23:47:08   \n",
              "17174            True  2020-07-24 23:47:12   \n",
              "17173           False  2020-07-24 23:47:12   \n",
              "17172           False  2020-07-24 23:47:12   \n",
              "17171           False  2020-07-24 23:47:13   \n",
              "...               ...                  ...   \n",
              "166660          False  2020-08-30 09:07:25   \n",
              "166659          False  2020-08-30 09:07:25   \n",
              "166658          False  2020-08-30 09:07:26   \n",
              "166657          False  2020-08-30 09:07:34   \n",
              "166656          False  2020-08-30 09:07:39   \n",
              "\n",
              "                                                     text     hashtags  \\\n",
              "17175   PROTESTORS NEEDED 6PM-10PM DAILY (NOW). 1450 E...          NaN   \n",
              "17174   Always honored to speak w @NicolleDWallace abo...  ['covid19']   \n",
              "17173   Excellent partnership to prevent #COVID19, tha...  ['COVID19']   \n",
              "17172   Don't take your eye off the ball. There are st...  ['COVID19']   \n",
              "17171   Apparently, at the Timberlea @saveonfoods stor...          NaN   \n",
              "...                                                   ...          ...   \n",
              "166660  In Germany, we have ~1-3 deaths/day while dail...          NaN   \n",
              "166659  I do find it interesting how the #Tories are b...   ['Tories']   \n",
              "166658  Concerns have been raised that return to unive...  ['COVID19']   \n",
              "166657  ⏰ Today @ 11:30!\\nTune in for the latest #Bach...     ['Bach']   \n",
              "166656  It’s a shame magots can’t celebrate @realDonal...          NaN   \n",
              "\n",
              "                     source  is_retweet  emotion               loc1  \\\n",
              "17175       Twitter Web App       False      joy             denton   \n",
              "17174       Twitter Web App       False  sadness           new york   \n",
              "17173   Twitter for Android       False      joy            seattle   \n",
              "17172   Twitter for Android       False      joy           victoria   \n",
              "17171       Twitter Web App       False  sadness            alberta   \n",
              "...                     ...         ...      ...                ...   \n",
              "166660      Twitter Web App       False      joy          rna world   \n",
              "166659  Twitter for Android       False  sadness            england   \n",
              "166658     Twitter for iPad       False  sadness  newman university   \n",
              "166657      Twitter Web App       False    anger        kings cross   \n",
              "166656   Twitter for iPhone       False  sadness             boston   \n",
              "\n",
              "                     loc2     anger       joy  optimism   sadness  \n",
              "17175               texas  0.234784  0.294793  0.235554  0.234869  \n",
              "17174                 usa  0.234835  0.235241  0.235095  0.294829  \n",
              "17173                  wa  0.235177  0.294583  0.235338  0.234902  \n",
              "17172    british columbia  0.239036  0.269476  0.241188  0.250300  \n",
              "17171              canada  0.240844  0.234815  0.235718  0.288623  \n",
              "...                   ...       ...       ...       ...       ...  \n",
              "166660               None  0.235192  0.294419  0.235536  0.234854  \n",
              "166659               None  0.245783  0.246070  0.253161  0.254986  \n",
              "166658                 uk  0.244661  0.235900  0.238270  0.281169  \n",
              "166657             london  0.268694  0.253983  0.241060  0.236263  \n",
              "166656                 ma  0.235014  0.236960  0.237581  0.290444  \n",
              "\n",
              "[179108 rows x 20 columns]"
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              "      <th>17175</th>\n",
              "      <td>Jessica Luther Rummel</td>\n",
              "      <td>denton, texas</td>\n",
              "      <td>Mother. Partner. Scholar. Activist. Alchemist....</td>\n",
              "      <td>2020-07-05 03:28:16</td>\n",
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              "      <td>False</td>\n",
              "      <td>2020-07-24 23:47:08</td>\n",
              "      <td>PROTESTORS NEEDED 6PM-10PM DAILY (NOW). 1450 E...</td>\n",
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              "      <td>0.235554</td>\n",
              "      <td>0.234869</td>\n",
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              "    <tr>\n",
              "      <th>17174</th>\n",
              "      <td>Dr. Lipi #TrustDrFauci Roy</td>\n",
              "      <td>new york, usa</td>\n",
              "      <td>@NBCNews @MSNBC @Forbes Medical Contributor | ...</td>\n",
              "      <td>2009-10-11 18:46:51</td>\n",
              "      <td>12485</td>\n",
              "      <td>4603</td>\n",
              "      <td>38120</td>\n",
              "      <td>True</td>\n",
              "      <td>2020-07-24 23:47:12</td>\n",
              "      <td>Always honored to speak w @NicolleDWallace abo...</td>\n",
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              "      <td>0.235095</td>\n",
              "      <td>0.294829</td>\n",
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              "      <th>17173</th>\n",
              "      <td>Patty Hayes</td>\n",
              "      <td>seattle, wa</td>\n",
              "      <td>Director of Public Health - Seattle &amp; King Cou...</td>\n",
              "      <td>2017-07-07 18:56:50</td>\n",
              "      <td>718</td>\n",
              "      <td>162</td>\n",
              "      <td>2276</td>\n",
              "      <td>False</td>\n",
              "      <td>2020-07-24 23:47:12</td>\n",
              "      <td>Excellent partnership to prevent #COVID19, tha...</td>\n",
              "      <td>['COVID19']</td>\n",
              "      <td>Twitter for Android</td>\n",
              "      <td>False</td>\n",
              "      <td>joy</td>\n",
              "      <td>seattle</td>\n",
              "      <td>wa</td>\n",
              "      <td>0.235177</td>\n",
              "      <td>0.294583</td>\n",
              "      <td>0.235338</td>\n",
              "      <td>0.234902</td>\n",
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              "    <tr>\n",
              "      <th>17172</th>\n",
              "      <td>Clive Gorman</td>\n",
              "      <td>victoria, british columbia</td>\n",
              "      <td>Marketing Director @CNEGames for @idlechampion...</td>\n",
              "      <td>2011-12-23 02:02:35</td>\n",
              "      <td>597</td>\n",
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              "      <td>23824</td>\n",
              "      <td>False</td>\n",
              "      <td>2020-07-24 23:47:12</td>\n",
              "      <td>Don't take your eye off the ball. There are st...</td>\n",
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              "      <td>Twitter for Android</td>\n",
              "      <td>False</td>\n",
              "      <td>joy</td>\n",
              "      <td>victoria</td>\n",
              "      <td>british columbia</td>\n",
              "      <td>0.239036</td>\n",
              "      <td>0.269476</td>\n",
              "      <td>0.241188</td>\n",
              "      <td>0.250300</td>\n",
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              "      <th>17171</th>\n",
              "      <td>Tristyn Russelo</td>\n",
              "      <td>alberta, canada</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-14 22:01:54</td>\n",
              "      <td>5</td>\n",
              "      <td>68</td>\n",
              "      <td>57</td>\n",
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              "      <td>2020-07-24 23:47:13</td>\n",
              "      <td>Apparently, at the Timberlea @saveonfoods stor...</td>\n",
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              "      <td>Dirk Haussecker</td>\n",
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              "      <td>2011-11-10 12:00:58</td>\n",
              "      <td>9341</td>\n",
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              "      <td>In Germany, we have ~1-3 deaths/day while dail...</td>\n",
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              "      <td>rna world</td>\n",
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              "      <td>0.294419</td>\n",
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              "      <td>2019-01-06 14:22:49</td>\n",
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              "      <td>Foster Dad, Youth Worker &amp; Senior Lecturer (Ch...</td>\n",
              "      <td>2015-06-20 10:56:58</td>\n",
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              "      <td>1140</td>\n",
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              "      <td>2020-08-30 09:07:26</td>\n",
              "      <td>Concerns have been raised that return to unive...</td>\n",
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              "      <td>0.235900</td>\n",
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              "      <td>2009-02-26 12:16:47</td>\n",
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              "      <td>23986</td>\n",
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              "      <td>2020-08-30 09:07:34</td>\n",
              "      <td>⏰ Today @ 11:30!\\nTune in for the latest #Bach...</td>\n",
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              "      <td>2009-04-13 13:14:46</td>\n",
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              "      <td>2020-08-30 09:07:39</td>\n",
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              "      <td>0.237581</td>\n",
              "      <td>0.290444</td>\n",
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