Copy_of_LSTM_IEEE.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sydney-machine-learning/COVID19-antivaccine-sentimentanalysis/blob/main/Copy_of_LSTM_IEEE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "f00BxTccqLYU",
        "outputId": "953732ec-36f1-4bba-b61b-e75e160249ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting torch==1.7.0\n",
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            "\u001b[31mERROR: torchvision 0.9.1+cu101 has requirement torch==1.8.1, but you'll have torch 1.7.0 which is incompatible.\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!pip install torch==1.7.0 \n",
        "!pip install torchtext==0.8.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_afZH3wtEp2"
      },
      "outputs": [],
      "source": [
        "#!pip install torchtext"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fdNVc_gJvddW"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "SEED = 1024\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed(SEED)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "\n",
        "from torch.utils.data import TensorDataset, DataLoader, Dataset\n",
        "import torchtext\n",
        "from torchtext import data\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import operator\n",
        "\n",
        "from sklearn.metrics import hamming_loss, jaccard_score, label_ranking_average_precision_score, f1_score\n",
        "from tqdm._tqdm_notebook import tqdm_notebook as tqdm"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BNLmpAEvhl3",
        "outputId": "4339029e-4ea2-41e3-9cd4-f99c6143fc60"
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          "text": [
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            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n"
          ]
        }
      ],
      "source": [
        "import spacy\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from wordcloud import WordCloud\n",
        "import re\n",
        "import unicodedata\n",
        "import nltk\n",
        "import csv\n",
        "import string\n",
        "from textblob import TextBlob\n",
        "from nltk.corpus import stopwords\n",
        "!pip install demoji\n",
        "nltk.download('all')\n",
        "import demoji"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhCxgtXVkkM9",
        "outputId": "c7f70ab5-0c32-4629-ca0d-4097ff9731fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.7.0\n"
          ]
        }
      ],
      "source": [
        "print(torch.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nH605tF7dGco",
        "outputId": "6838f0c5-86e0-46da-9880-f4533b87498b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading emoji data ...\n",
            "... OK (Got response in 0.21 seconds)\n",
            "Writing emoji data to /root/.demoji/codes.json ...\n",
            "... OK\n"
          ]
        }
      ],
      "source": [
        "demoji.download_codes()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "FPx_3xdwvmgF",
        "outputId": "c13c4ff5-cbcb-4842-98ae-bbba4239d8f5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Dpbdm3zuxt9T",
        "outputId": "a817fdf3-c904-4bcf-ed9d-4a5c7bbabd92"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                               tweet sentiment_score    date\n",
            "0   all respected members  a team of different co...     0.053333333  Apr-17\n",
            "1  ok here’s a question   i believe we r gonna ov...            0.25  Apr-17\n",
            "2  well  everywhere is talking about coronapeople...               0  Apr-17\n",
            "3  like combined global corona infection dashboar...               0  Apr-17\n",
            "4   they are already talking about how a corona v...              -1  Apr-17\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "tweet_df = pd.read_csv('/content/gdrive/My Drive/data with no geo tag/merged_mar_to_aug.csv', lineterminator='\\r')\n",
        "tweet_df = tweet_df.replace('\\n','', regex=True)\n",
        "tweet_df = tweet_df.dropna()\n",
        "tweet_df = tweet_df.drop(['label'], axis = 1)\n",
        "print(tweet_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ztnocSkodeyY"
      },
      "outputs": [],
      "source": [
        "def emoji2text(tweet):\n",
        "        emojis = demoji.findall(tweet)\n",
        "        new_tweet = []\n",
        "        for word in tweet.split():\n",
        "            if word in emojis.keys():\n",
        "                tweet = tweet.replace(word, emojis[word])\n",
        "                new_tweet.append(emojis[word])\n",
        "            wordmojis = demoji.findall(word)\n",
        "            for char in word:\n",
        "                if char in wordmojis.keys():\n",
        "                    tweet = tweet.replace(word, wordmojis[char])\n",
        "        return tweet\n",
        "\n",
        "tweet_df['tweet'] = tweet_df['tweet'].apply(lambda x : emoji2text(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Yosm3lJpx0qj"
      },
      "outputs": [],
      "source": [
        "class CustomLSTM(nn.Module):\n",
        "    def __init__(self, embedding_dim, vocab, hidden_dim, output_dim, drop_prob, bidirectional = False, use_glove = True):\n",
        "        super().__init__()\n",
        "        self.embeddings = nn.Embedding(len(vocab), embedding_dim)\n",
        "        if use_glove:\n",
        "            self.embeddings.weight.data.copy_(vocab.vectors)\n",
        "            self.embeddings.weight.requires_grad = False\n",
        "        self.drop_prob = drop_prob\n",
        "\n",
        "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, bidirectional = bidirectional, batch_first = True, num_layers = 2)\n",
        "        if bidirectional is True:\n",
        "            self.lin = nn.Linear(2*hidden_dim, 64)\n",
        "        else:\n",
        "            self.lin = nn.Linear(hidden_dim, 64)\n",
        "        self.fc = nn.Linear(64, output_dim)\n",
        "        self.dropout = nn.Dropout(p = drop_prob)\n",
        "    def forward(self, sentence):\n",
        "        #sentence = [max_len, batch_size]\n",
        "\n",
        "        embed = self.embeddings(torch.transpose(sentence, 0, 1))\n",
        "        #embed = [batch_size, max_len, embedding_dim]\n",
        "        \n",
        "        if self.drop_prob:\n",
        "            embed = self.dropout(embed)\n",
        "        \n",
        "        lstm_out, (hidden, cell) = self.lstm(embed)\n",
        "        #lstm_out = [batch_size, max_len, 2*hidden_dim if bidirectional else hidden_dim]\n",
        "        #hidden = [num_layers, batch_size, hidden_dim]\n",
        "        #cell = [num_layers, batch_size, hidden_dim]\n",
        "        \n",
        "        out = lstm_out[:,-1,:].squeeze()\n",
        "        #out = [batch_size, 2*hidden_dim if bidirectional else hidden_dim]\n",
        "        \n",
        "        out = self.lin(out)\n",
        "        #out = [batch_size, 64]\n",
        "\n",
        "        outputs = self.fc(out)\n",
        "        #outputs = [batch_size, output_dim]\n",
        "        \n",
        "        return outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "nO7YPS7Tx4Ak"
      },
      "outputs": [],
      "source": [
        "def evaluation_metrics(actual_labels, pred_labels, threshold):\n",
        "    int_pred_labels = pred_labels\n",
        "    for i in range(len(pred_labels)):\n",
        "        for j in range(11):\n",
        "            if int_pred_labels[i][j] >= threshold: int_pred_labels[i][j] = 1\n",
        "            else:\n",
        "                int_pred_labels[i][j] = 0\n",
        "    \n",
        "    ham_loss = hamming_loss(actual_labels, int_pred_labels)\n",
        "    jacc_score = jaccard_score(actual_labels, int_pred_labels, average = 'samples')\n",
        "    lrap = label_ranking_average_precision_score(actual_labels, pred_labels)\n",
        "    f1_macro = f1_score(actual_labels, int_pred_labels, average = 'macro')\n",
        "    f1_micro = f1_score(actual_labels, int_pred_labels, average = 'micro')\n",
        "\n",
        "    return ham_loss, jacc_score, lrap, f1_macro, f1_micro"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kiQrxIdqx6Wt",
        "outputId": "e83e48f6-8096-44c6-d8d0-f6469a2d094e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CustomLSTM(\n",
              "  (embeddings): Embedding(13666, 300)\n",
              "  (lstm): LSTM(300, 128, num_layers=2, batch_first=True)\n",
              "  (lin): Linear(in_features=128, out_features=64, bias=True)\n",
              "  (fc): Linear(in_features=64, out_features=11, bias=True)\n",
              "  (dropout): Dropout(p=0.65, inplace=False)\n",
              ")"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = torch.load('/content/gdrive/My Drive/model7LSTM.pth',map_location ='cpu')\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j1coPSevx8UG"
      },
      "outputs": [],
      "source": [
        "import spacy\n",
        "spacy_en = spacy.load('en')\n",
        "\n",
        "def tokenizer(tweet):\n",
        "    tweet = re.sub(r'[\\n]', ' ', tweet)\n",
        "    return [tok.text for tok in spacy_en.tokenizer(tweet)]\n",
        "\n",
        "tweet_field = data.Field(sequential = True, lower = True, tokenize = tokenizer)\n",
        "dataFields = [(\"tweet\", tweet_field), (\"sentiment_score\", None),(\"date\", None)]\n",
        "\n",
        "train_dataset = data.TabularDataset(\n",
        "    path = '/content/gdrive/My Drive/data with no geo tag/merged_mar_to_aug.csv', format = 'csv', fields = dataFields, skip_header = True\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T435IxsEyA4u"
      },
      "outputs": [],
      "source": [
        "print(train_dataset) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8lTb3mMvyBBW"
      },
      "outputs": [],
      "source": [
        "print(\"length of dataset:\", len(train_dataset))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "74MEjTLUyBE-"
      },
      "outputs": [],
      "source": [
        "def check_coverage(vocab, embeddings_index):\n",
        "    a = {}\n",
        "    oov = {}\n",
        "    k = 0\n",
        "    i = 0\n",
        "    for word in tqdm(vocab):\n",
        "        try:\n",
        "            a[word] = embeddings_index[word]\n",
        "            k += vocab[word]\n",
        "        except:\n",
        "            oov[word] = vocab[word]\n",
        "            i += vocab[word]\n",
        "            pass\n",
        "        \n",
        "    print('Found embeddings for {:.2%} of vocab'.format(len(a) / len(vocab)))\n",
        "    print('Found embeddings for  {:.2%} of all text'.format(k / (k + i)))\n",
        "    sorted_x = sorted(oov.items(), key = operator.itemgetter(1))[::-1]\n",
        "\n",
        "    return sorted_x\n",
        "\n",
        "def build_vocab(sentences, verbose = True):\n",
        "    vocab = {}\n",
        "    for sentence in tqdm(sentences, disable = (not verbose)):\n",
        "        for word in sentence:\n",
        "            try:\n",
        "                vocab[word] += 1\n",
        "            except:\n",
        "                vocab[word] = 1\n",
        "    return vocab\n",
        "    \n",
        "def get_coefs(word, *arr):\n",
        "    return word, np.asarray(arr, dtype='float32')\n",
        "\n",
        "\n",
        "def load_embeddings(path):\n",
        "    with open(path, encoding = \"utf-8\") as f:\n",
        "        return dict(get_coefs(*line.strip().split(' ')) for line in f)\n",
        "\n",
        "\n",
        "def build_matrix(word_index, path):\n",
        "    embedding_index = load_embeddings(path)\n",
        "    embedding_matrix = np.zeros((len(word_index) + 1, 300))\n",
        "    for word, i in word_index.items():\n",
        "        try:\n",
        "            embedding_matrix[i] = embedding_index[word]\n",
        "        except KeyError:\n",
        "            pass\n",
        "    return embedding_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gICzlHhqyBHt"
      },
      "outputs": [],
      "source": [
        "GLOVE_EMBEDDING_FILE = '/content/gdrive/My Drive/glove.840B.300d.txt'\n",
        "glove_embeddings = load_embeddings(GLOVE_EMBEDDING_FILE)\n",
        "print(f'loaded {len(glove_embeddings)} word vectors ')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1rSVL4WxyfyS"
      },
      "outputs": [],
      "source": [
        "print(list(tweet_df))\n",
        "print(tweet_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hieduDfYykOj"
      },
      "outputs": [],
      "source": [
        "vocab = build_vocab(list(tweet_df['tweet'].apply(lambda x : x.split())))\n",
        "oov = check_coverage(vocab, glove_embeddings)\n",
        "oov[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vvd8tjVhykWm"
      },
      "outputs": [],
      "source": [
        "TWEET = data.Field(sequential = True, lower = True, tokenize = tokenizer)\n",
        "LABEL = data.Field(sequential = False, use_vocab = False)\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "TWEET.build_vocab(train_dataset, vectors = 'glove.840B.300d')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-I2SMZhCykbp"
      },
      "outputs": [],
      "source": [
        "tweet_field.vocab = TWEET.vocab\n",
        "BATCH_SIZE = 1\n",
        "n_label = 11\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "train_iter = data.BucketIterator(train_dataset,\n",
        "                                batch_size = BATCH_SIZE,\n",
        "                                sort_key = lambda x : len(x.tweet),\n",
        "                                sort_within_batch = False,\n",
        "                                repeat = False,\n",
        "                                shuffle = False,\n",
        "                                device = device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8WIUNRcVykeQ"
      },
      "outputs": [],
      "source": [
        "\n",
        "class BatchWrapper():\n",
        "    def __init__(self, dl, x_var, y_vars):\n",
        "        self.dl = dl\n",
        "        self.x_var = x_var\n",
        "        self.y_vars = y_vars\n",
        "\n",
        "    def __iter__(self):\n",
        "        for batch in self.dl:\n",
        "            x = getattr(batch, self.x_var)\n",
        "            if self.y_vars is not None:\n",
        "                y = torch.cat([getattr(batch, feat).unsqueeze(1) for feat in self.y_vars], dim = 1).float()\n",
        "            else:\n",
        "                y = torch.zeros((1))\n",
        "            yield(x, y)\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.dl)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KO9ZyKeGykhT"
      },
      "outputs": [],
      "source": [
        "train_dl = BatchWrapper(train_iter, \"tweet\", None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zUqsu-NkykjJ"
      },
      "outputs": [],
      "source": [
        "i = 0\n",
        "for x, y in train_dl:\n",
        "    print(x.squeeze())\n",
        "    i += 1\n",
        "    if i == 10: break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w2s1G8JJnAWF"
      },
      "outputs": [],
      "source": [
        "print(train_dl)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DSruIDnoyung"
      },
      "outputs": [],
      "source": [
        "predicted_labels = []\n",
        "sus = []\n",
        "i = 0\n",
        "for X, y in train_dl:\n",
        "    i += 1\n",
        "    if i not in sus:\n",
        "        preds = model(X)\n",
        "        sig = nn.Sigmoid()\n",
        "        out = sig(preds)\n",
        "\n",
        "        out[out >= 0.5] = 1\n",
        "        out[out < 0.5] = 0\n",
        "        predicted_labels.append(out.detach().cpu().numpy().tolist())\n",
        "        if i % 100 == 0:\n",
        "            print(\"{}/{} iterations done\".format(i, len(train_dl)))\n",
        "    else:\n",
        "        predicted_labels.append([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])\n",
        "    if i == 181500:\n",
        "      break "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RoqH4Q_Kyuqa"
      },
      "outputs": [],
      "source": [
        "print(len(predicted_labels))\n",
        "till_now = predicted_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KJUVc8Uqyuti"
      },
      "outputs": [],
      "source": [
        "classified_df = pd.read_csv('/content/gdrive/My Drive/data with no geo tag/merged_mar_to_aug.csv', lineterminator='\\r')\n",
        "classified_df = tweet_df.replace('\\n','', regex=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aQx5pKmdyuwY"
      },
      "outputs": [],
      "source": [
        "classified_df['Optimistic'] = \"None\"\n",
        "classified_df['Thankful'] = \"None\"\n",
        "classified_df['Empathetic'] = \"None\"\n",
        "classified_df['Pessimistic'] = \"None\"\n",
        "classified_df['Anxious'] = \"None\"\n",
        "classified_df['Sad'] = \"None\"\n",
        "classified_df['Annoyed'] = \"None\"\n",
        "classified_df['Denial'] = \"None\"\n",
        "classified_df['Official report'] = \"None\"\n",
        "classified_df['Surprise'] = \"None\"\n",
        "classified_df['Joking'] = \"None\"\n",
        "print(classified_df.head(5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HkTWOR13yuyb"
      },
      "outputs": [],
      "source": [
        "for i in range(len(till_now)):\n",
        "    classified_df['Optimistic'].iloc[i] = till_now[i][0]\n",
        "    classified_df['Thankful'].iloc[i] = till_now[i][1]\n",
        "    classified_df['Empathetic'].iloc[i] = till_now[i][2]\n",
        "    classified_df['Pessimistic'].iloc[i] = till_now[i][3]\n",
        "    classified_df['Anxious'].iloc[i] = till_now[i][4]\n",
        "    classified_df['Sad'].iloc[i] = till_now[i][5]\n",
        "    classified_df['Annoyed'].iloc[i] = till_now[i][6]\n",
        "    classified_df['Denial'].iloc[i] = till_now[i][7]\n",
        "    classified_df['Official report'].iloc[i] = till_now[i][8]\n",
        "    classified_df['Surprise'].iloc[i] = till_now[i][9]\n",
        "    classified_df['Joking'].iloc[i] = till_now[i][10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P7yHxgMTy3kZ"
      },
      "outputs": [],
      "source": [
        "classified_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o5Ny_ePOMRIJ"
      },
      "outputs": [],
      "source": [
        "classified_df.to_csv(r'classified_df.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZNMgu4Mcy3nb"
      },
      "outputs": [],
      "source": [
        "rand_text = classified_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SL0WXY03y3pr"
      },
      "outputs": [],
      "source": [
        "rand_idx = np.random.randint(0, 5508)\n",
        "print(rand_idx)\n",
        "print(classified_df.iloc[rand_idx]['tweet'])\n",
        "print(classified_df.iloc[rand_idx])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n_vk1gDuEzdv"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NPepxWVPy3r-"
      },
      "outputs": [],
      "source": [
        "categories = list(classified_df.columns[2:].values)\n",
        "sns.set(font_scale = 2)\n",
        "\n",
        "plt.figure(figsize = (15, 8))\n",
        "ax= sns.barplot(classified_df.iloc[:, 2:].sum().values, categories, orient = 'h')\n",
        "plt.title(\"Distribution of Emotions\", fontsize = 24)\n",
        "plt.xlabel('Number of Tweets', fontsize = 18)\n",
        "plt.ylabel('Emotion type', fontsize = 18)\n",
        "\n",
        "#adding the text labels\n",
        "rects = ax.patches\n",
        "labels = classified_df.iloc[:, 2:].sum().values\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    #ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha = 'center', va = 'bottom', fontsize = 18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PK310SdEknGP"
      },
      "outputs": [],
      "source": [
        "from wordcloud import WordCloud, STOPWORDS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RgHj8KuHko59"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8IZve4_fkpko"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "huV9AExgkpqj"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u0DGjQoKkpwt"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Copy of LSTM IEEE.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOciq6SDPZrKbGJTHK9DUXn",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}