Multiclass Text Classification with DistilBERT on COVID-19 Tweets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0280ad65",
   "metadata": {},
   "source": [
    "# Coronavirus tweets NLP - Text Classification\n",
    "### Corona Virus Tagged Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b958550f",
   "metadata": {},
   "source": [
    "**Task**  \n",
    "Perform Text Classification on the tweets pulled from Twitter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed108fa4",
   "metadata": {},
   "source": [
    "**Columns:**\n",
    "- Location  \n",
    "- Tweet At  \n",
    "- Original Tweet  \n",
    "- Label  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fa8f69",
   "metadata": {},
   "source": [
    "**Models**  \n",
    "I used a baseline Naive Bayes model and a Neural Network archiecture using DistilBERT as the embedding layer with a LSTM and Dense layers following. I used huggingface's transformers library and tensorflow for the deep learning model and scikit-learn for the naive bayes model. I also used GPU acceleration through a GTX 1060 (6GB) for training the neural network. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7e5d9d",
   "metadata": {},
   "source": [
    "**Link to data:**  \n",
    "https://www.kaggle.com/datatattle/covid-19-nlp-text-classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b343588e",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28afa396",
   "metadata": {},
   "source": [
    "**Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9843848f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import string\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm # Progress Bar\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "import transformers\n",
    "from transformers import DistilBertTokenizer, TFDistilBertModel, DistilBertConfig\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
    "import warnings\n",
    "from transformers import logging as hf_logging\n",
    "hf_logging.set_verbosity_error() # Hidding Huggingface Warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ddddfb3",
   "metadata": {},
   "source": [
    "Enabling GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "005c32e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "  try:\n",
    "    # Currently, memory growth needs to be the same across GPUs\n",
    "    for gpu in gpus:\n",
    "      tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "    print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "  except RuntimeError as e:\n",
    "    # Memory growth must be set before GPUs have been initialized\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4fed802",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83768e2d",
   "metadata": {},
   "source": [
    "**DistilBERT Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eca1f0f",
   "metadata": {},
   "source": [
    "https://huggingface.co/distilbert-base-uncased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5041aeaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_NAME = 'distilbert-base-cased'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accd5142",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "289f0458",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('./Corona_NLP_test.csv', encoding = \"UTF-8\", encoding_errors='ignore')\n",
    "train_df = pd.read_csv('./Corona_NLP_train.csv', encoding = \"UTF-8\", encoding_errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb48c57",
   "metadata": {},
   "source": [
    "*Dropping any duplicates or missing values*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e377c838",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop_duplicates(inplace= True)\n",
    "test_df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b2466fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserName            0\n",
       "ScreenName          0\n",
       "Location         8590\n",
       "TweetAt             0\n",
       "OriginalTweet       0\n",
       "Sentiment           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "94c98c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserName           0\n",
       "ScreenName         0\n",
       "Location         834\n",
       "TweetAt            0\n",
       "OriginalTweet      0\n",
       "Sentiment          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89fd2c92",
   "metadata": {},
   "source": [
    "Because a missing location is not as important as a missing tweet or sentiment, I will not remove those entires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69ab8375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 41157 entries in the training dataframe\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>UserName</th>\n",
       "      <th>ScreenName</th>\n",
       "      <th>Location</th>\n",
       "      <th>TweetAt</th>\n",
       "      <th>OriginalTweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3799</td>\n",
       "      <td>48751</td>\n",
       "      <td>London</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3800</td>\n",
       "      <td>48752</td>\n",
       "      <td>UK</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>advice Talk to your neighbours family to excha...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3801</td>\n",
       "      <td>48753</td>\n",
       "      <td>Vagabonds</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Coronavirus Australia: Woolworths to give elde...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3802</td>\n",
       "      <td>48754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>My food stock is not the only one which is emp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3803</td>\n",
       "      <td>48755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Me, ready to go at supermarket during the #COV...</td>\n",
       "      <td>Extremely Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserName  ScreenName   Location     TweetAt  \\\n",
       "0      3799       48751     London  16-03-2020   \n",
       "1      3800       48752         UK  16-03-2020   \n",
       "2      3801       48753  Vagabonds  16-03-2020   \n",
       "3      3802       48754        NaN  16-03-2020   \n",
       "4      3803       48755        NaN  16-03-2020   \n",
       "\n",
       "                                       OriginalTweet           Sentiment  \n",
       "0  @MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...             Neutral  \n",
       "1  advice Talk to your neighbours family to excha...            Positive  \n",
       "2  Coronavirus Australia: Woolworths to give elde...            Positive  \n",
       "3  My food stock is not the only one which is emp...            Positive  \n",
       "4  Me, ready to go at supermarket during the #COV...  Extremely Negative  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"There are {train_df.shape[0]} entries in the training dataframe\")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca726c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3798 entries in the test dataframe\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>UserName</th>\n",
       "      <th>ScreenName</th>\n",
       "      <th>Location</th>\n",
       "      <th>TweetAt</th>\n",
       "      <th>OriginalTweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>44953</td>\n",
       "      <td>NYC</td>\n",
       "      <td>02-03-2020</td>\n",
       "      <td>TRENDING: New Yorkers encounter empty supermar...</td>\n",
       "      <td>Extremely Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44954</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>02-03-2020</td>\n",
       "      <td>When I couldn't find hand sanitizer at Fred Me...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>44955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02-03-2020</td>\n",
       "      <td>Find out how you can protect yourself and love...</td>\n",
       "      <td>Extremely Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>44956</td>\n",
       "      <td>Chicagoland</td>\n",
       "      <td>02-03-2020</td>\n",
       "      <td>#Panic buying hits #NewYork City as anxious sh...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>44957</td>\n",
       "      <td>Melbourne, Victoria</td>\n",
       "      <td>03-03-2020</td>\n",
       "      <td>#toiletpaper #dunnypaper #coronavirus #coronav...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserName  ScreenName             Location     TweetAt  \\\n",
       "0         1       44953                  NYC  02-03-2020   \n",
       "1         2       44954          Seattle, WA  02-03-2020   \n",
       "2         3       44955                  NaN  02-03-2020   \n",
       "3         4       44956          Chicagoland  02-03-2020   \n",
       "4         5       44957  Melbourne, Victoria  03-03-2020   \n",
       "\n",
       "                                       OriginalTweet           Sentiment  \n",
       "0  TRENDING: New Yorkers encounter empty supermar...  Extremely Negative  \n",
       "1  When I couldn't find hand sanitizer at Fred Me...            Positive  \n",
       "2  Find out how you can protect yourself and love...  Extremely Positive  \n",
       "3  #Panic buying hits #NewYork City as anxious sh...            Negative  \n",
       "4  #toiletpaper #dunnypaper #coronavirus #coronav...             Neutral  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"There are {test_df.shape[0]} entries in the test dataframe\")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23a3039a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reorder = ['Extremely Positive', 'Positive', 'Neutral', 'Negative', 'Extremely Negative']\n",
    "\n",
    "fig, axes = plt.subplots(1,2 , figsize=(10,5))\n",
    "\n",
    "train_df.value_counts('Sentiment').reindex(index=reorder).plot(kind='bar', ax=axes[0])\n",
    "plt.sca(axes[0])\n",
    "plt.xticks(rotation=45, horizontalalignment='right')\n",
    "plt.title('Training Dataset')\n",
    "plt.ylabel('Counts')\n",
    "\n",
    "test_df.value_counts('Sentiment').reindex(index=reorder).plot(kind='bar', ax=axes[1])\n",
    "plt.sca(axes[1])\n",
    "plt.xticks(rotation=45, horizontalalignment='right')\n",
    "plt.title('Testing Dataset');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde60c01",
   "metadata": {},
   "source": [
    "It looks like the training and testsing datasets are not balanced across all categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a992b6ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The max word count is 64\n"
     ]
    }
   ],
   "source": [
    "max_words = []\n",
    "for row in train_df['OriginalTweet']:\n",
    "    max_words.append(len(row.split()))\n",
    "print(f\"The max word count is {np.asarray(max_words).max()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f30183b8",
   "metadata": {},
   "source": [
    "**Getting rid of the URL's in the tweets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "698fb0e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRENDING: New Yorkers encounter empty supermarket shelves (pictured, Wegmans in Brooklyn), sold-out online grocers (FoodKick, MaxDelivery) as #coronavirus-fearing shoppers stock up https://t.co/Gr76pcrLWh https://t.co/ivMKMsqdT1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'TRENDING: New Yorkers encounter empty supermarket shelves (pictured, Wegmans in Brooklyn), sold-out online grocers (FoodKick, MaxDelivery) as #coronavirus-fearing shoppers stock up  '"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_str = test_df['OriginalTweet'][0]\n",
    "print(test_str)\n",
    "\n",
    "def remove_https(row):\n",
    "    return re.sub(r'http\\S+', '', row)\n",
    "\n",
    "remove_https(test_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eeeeee0",
   "metadata": {},
   "source": [
    "*Apply to whole datasets*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "caee95cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['OriginalTweet_no_URL'] = train_df['OriginalTweet'].apply(remove_https)\n",
    "test_df['OriginalTweet_no_URL'] = test_df['OriginalTweet'].apply(remove_https)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75dd90a5",
   "metadata": {},
   "source": [
    "**Label Encode the datasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf5dd59a",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "train_df['Sentiment_label'] = le.fit_transform(train_df['Sentiment'])\n",
    "test_df['Sentiment_label'] = le.transform(test_df['Sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ec046f31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Extremely Negative\n",
      "1 Extremely Positive\n",
      "2 Negative\n",
      "3 Neutral\n",
      "4 Positive\n"
     ]
    }
   ],
   "source": [
    "label_dict = (test_df[['Sentiment','Sentiment_label']].drop_duplicates()\n",
    "              .sort_values(by='Sentiment_label')\n",
    "              .reset_index(drop=True)['Sentiment']\n",
    "              .to_dict())\n",
    "\n",
    "for index, key in label_dict.items():\n",
    "    print(index, key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef219fca",
   "metadata": {},
   "source": [
    "**Splitting the data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9733cae1",
   "metadata": {},
   "source": [
    "I will take 10% of the training data as validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f7e8393b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37041 training examples\n",
      "4116 validation examples\n"
     ]
    }
   ],
   "source": [
    "# Get the number training samples\n",
    "num_train_samples = int(train_df.shape[0] * 0.9)\n",
    "\n",
    "x_train = train_df['OriginalTweet_no_URL'][:num_train_samples]\n",
    "y_train = train_df['Sentiment_label'][:num_train_samples]\n",
    "x_val = train_df['OriginalTweet_no_URL'][num_train_samples:]\n",
    "y_val = train_df['Sentiment_label'][num_train_samples:]\n",
    "\n",
    "### Sanity Check\n",
    "assert(len(x_train) + len(x_val) == train_df.shape[0])\n",
    "\n",
    "print(f\"{len(x_train)} training examples\\n{len(x_val)} validation examples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c0fcac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3798 testing examples\n"
     ]
    }
   ],
   "source": [
    "x_test = test_df['OriginalTweet_no_URL']\n",
    "y_test = test_df['Sentiment_label']\n",
    "print(f\"{len(x_test)} testing examples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1630270",
   "metadata": {},
   "source": [
    "## Naive Bayes (Baseline Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a71d16",
   "metadata": {},
   "source": [
    "Pre-processing by:  \n",
    "- remove punctuations (except #)\n",
    "- lowercase all words\n",
    "- removing stopwords\n",
    "- stemming words"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "572318e1",
   "metadata": {},
   "source": [
    "*Note: I will not remove the **#** symbol because it is a predominant part of what makes tweets unique*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5352bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "porter = PorterStemmer()\n",
    "\n",
    "# Remove all puncutation\n",
    "def clean_responses(text):\n",
    "    ### Remove all puncutation\n",
    "    string.punctuation = string.punctuation.replace('#','')\n",
    "    for punctuation in string.punctuation:\n",
    "        text = text.replace(punctuation, '')\n",
    "    \n",
    "    ### Lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    ### Removing stop words\n",
    "    filtered_words = []\n",
    "    for word in text.split():\n",
    "        if word not in stop_words:\n",
    "            filtered_words.append(word)\n",
    "            \n",
    "    ### Stemming words\n",
    "    stemmed = [porter.stem(word) for word in filtered_words]\n",
    "                \n",
    "    return ' '.join(stemmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e737de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_NB = train_df['OriginalTweet_no_URL'].apply(clean_responses)\n",
    "y_train_NB = train_df['Sentiment_label']\n",
    "X_test_NB = test_df['OriginalTweet_no_URL'].apply(clean_responses)\n",
    "y_test_NB = test_df['Sentiment_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ef608dcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
    "model.fit(X_train_NB, y_train_NB)\n",
    "\n",
    "### Make predictions\n",
    "y_hat = model.predict(X_test_NB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81796671",
   "metadata": {},
   "source": [
    "**Results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e9ffe19b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.3451816745655608\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test_NB, y_hat))\n",
    "labels_5 = ['Extremely Negative','Extremely Positive','Negative','Neutral','Positive'] \n",
    "\n",
    "def print_cf1(y_test, y_hat):\n",
    "    cm = confusion_matrix(y_test, y_hat)\n",
    "    sns.set(font_scale = 1.4, color_codes=True, palette=\"deep\")\n",
    "    sns.heatmap(pd.DataFrame(cm, index=labels_5,columns=[0,1,2,3,4]), \n",
    "                annot = True,\n",
    "                annot_kws = {\"size\":16},\n",
    "                fmt=\"d\",\n",
    "                cmap=\"YlGnBu\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.xlabel(\"Predicted Value\")\n",
    "    plt.xticks([0,1,2,3,4], labels_5, rotation=45)\n",
    "    plt.ylabel(\"True Value\")\n",
    "    plt.show()\n",
    "    \n",
    "print_cf1(y_test_NB, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00673b1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Extremely Negative       0.67      0.00      0.01       592\n",
      "Extremely Positive       1.00      0.03      0.06       599\n",
      "          Negative       0.40      0.40      0.40      1041\n",
      "           Neutral       0.82      0.06      0.11       619\n",
      "          Positive       0.31      0.89      0.46       947\n",
      "\n",
      "          accuracy                           0.35      3798\n",
      "         macro avg       0.64      0.28      0.21      3798\n",
      "      weighted avg       0.58      0.35      0.25      3798\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_NB, y_hat, target_names=labels_5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff4bdd7",
   "metadata": {},
   "source": [
    "## DistilBERT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b7a46a8",
   "metadata": {},
   "source": [
    "**Tokenize the Inputs** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d0069b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = DistilBertTokenizer.from_pretrained(MODEL_NAME) # Loading the tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b90015",
   "metadata": {},
   "source": [
    "*Finding the max token sequence in the training set*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a7011c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum amount of tokens in the dataset is 232\n"
     ]
    }
   ],
   "source": [
    "max_val = 0\n",
    "for sent in (x_train.tolist() + x_train.tolist() + x_val.tolist()):\n",
    "    try:\n",
    "        sent_tok_len = len(tokenizer.tokenize(sent))\n",
    "        max_val = sent_tok_len if (sent_tok_len > max_val) else max_val\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "print(f\"The maximum amount of tokens in the dataset is {max_val}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b662020",
   "metadata": {},
   "source": [
    "I will set the max length and input for the deep learning model to 234  \n",
    "*(Max token length + 2 for special characters [CLS] and [SEP])*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1df84af",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LENGTH = 234\n",
    "\n",
    "tokenizer = DistilBertTokenizer.from_pretrained(MODEL_NAME,  \n",
    "                                                add_special_tokens=True,\n",
    "                                                max_length=MAX_LENGTH, \n",
    "                                                pad_to_max_length=True)\n",
    "\n",
    "def tokenize(sentences, tokenizer):\n",
    "    input_ids, input_masks, input_segments = [], [], []\n",
    "    for sentence in tqdm(sentences):\n",
    "        inputs = tokenizer.encode_plus(sentence, \n",
    "                                       add_special_tokens=True, \n",
    "                                       max_length=MAX_LENGTH, \n",
    "                                       pad_to_max_length=True, \n",
    "                                       return_attention_mask=True, \n",
    "                                       return_token_type_ids=True, \n",
    "                                       truncation=True)\n",
    "        input_ids.append(inputs['input_ids'])\n",
    "        input_masks.append(inputs['attention_mask'])\n",
    "        input_segments.append(inputs['token_type_ids'])        \n",
    "        \n",
    "    return np.asarray(input_ids, dtype='int32'), np.asarray(input_masks, dtype='int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "29156f79",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 37041/37041 [00:30<00:00, 1211.93it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 3798/3798 [00:03<00:00, 1088.73it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 4116/4116 [00:03<00:00, 1285.07it/s]\n"
     ]
    }
   ],
   "source": [
    "X_train = tokenize(x_train, tokenizer)\n",
    "X_test = tokenize(x_test, tokenizer)\n",
    "X_val = tokenize(x_val, tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9676c1",
   "metadata": {},
   "source": [
    "**Looking at entry**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "317d701c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive \n",
      "\n",
      "As news of the regions first confirmed COVID-19 case came out of Sullivan County last week, people flocked to area stores to purchase cleaning supplies, hand sanitizer, food, toilet paper and other goods, @Tim_Dodson reports  \n",
      "\n",
      "ID's\tInput Tokens\n",
      "101\t[CLS]\n",
      "1249\tAs\n",
      "2371\tnews\n",
      "1104\tof\n",
      "1103\tthe\n",
      "4001\tregions\n",
      "1148\tfirst\n",
      "3659\tconfirmed\n",
      "18732\tCO\n",
      "23314\t##VI\n",
      "2137\t##D\n",
      "118\t-\n",
      "1627\t19\n",
      "1692\tcase\n",
      "1338\tcame\n",
      "1149\tout\n",
      "1104\tof\n",
      "7124\tSullivan\n",
      "1391\tCounty\n",
      "1314\tlast\n",
      "1989\tweek\n",
      "117\t,\n",
      "1234\tpeople\n",
      "24117\tflock\n",
      "1174\t##ed\n",
      "1106\tto\n",
      "1298\tarea\n",
      "4822\tstores\n",
      "1106\tto\n",
      "4779\tpurchase\n",
      "9374\tcleaning\n",
      "5508\tsupplies\n",
      "117\t,\n",
      "1289\thand\n",
      "21718\tsa\n",
      "2605\t##ni\n",
      "3121\t##ti\n",
      "6198\t##zer\n",
      "117\t,\n",
      "2094\tfood\n",
      "117\t,\n",
      "12356\ttoilet\n",
      "2526\tpaper\n",
      "1105\tand\n",
      "1168\tother\n",
      "4817\tgoods\n",
      "117\t,\n",
      "137\t@\n",
      "4446\tTim\n",
      "168\t_\n",
      "2091\tDo\n",
      "21369\t##dson\n",
      "3756\treports\n",
      "102\t[SEP]\n"
     ]
    }
   ],
   "source": [
    "index_num = 5\n",
    "\n",
    "print(train_df.loc[index_num]['Sentiment'], '\\n')\n",
    "\n",
    "print(x_train[index_num], '\\n')\n",
    "\n",
    "temp_tokens = tokenizer.tokenize(x_train[index_num])\n",
    "temp_ids = tokenizer.encode(x_train[index_num])\n",
    "\n",
    "print('ID\\'s', 'Input Tokens', sep='\\t')\n",
    "for i in range(len(temp_ids)):\n",
    "    if i == 0:\n",
    "        print(temp_ids[i], '[CLS]', sep='\\t')\n",
    "        continue\n",
    "    if i == len(temp_ids)-1:\n",
    "        print(temp_ids[i], '[SEP]', sep='\\t')\n",
    "        break\n",
    "    print(temp_ids[i], temp_tokens[i-1], sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff836804",
   "metadata": {},
   "source": [
    "### Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e14727c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_token (InputLayer)        [(None, 234)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "masked_token (InputLayer)       [(None, 234)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "tf_distil_bert_model_1 (TFDisti TFBaseModelOutput(la 65190912    input_token[0][0]                \n",
      "                                                                 masked_token[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 234, 256)     918528      tf_distil_bert_model_1[0][13]    \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 256)          0           bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 64)           16448       global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_39 (Dropout)            (None, 64)           0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 5)            325         dropout_39[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 66,126,213\n",
      "Trainable params: 935,301\n",
      "Non-trainable params: 65,190,912\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "config = DistilBertConfig.from_pretrained(MODEL_NAME, output_hidden_states=True, output_attentions=True)\n",
    "DistilBERT = TFDistilBertModel.from_pretrained(MODEL_NAME, config=config)\n",
    "\n",
    "input_ids_in = tf.keras.layers.Input(shape=(MAX_LENGTH,), name='input_token', dtype='int32')\n",
    "input_masks_in = tf.keras.layers.Input(shape=(MAX_LENGTH,), name='masked_token', dtype='int32') \n",
    "\n",
    "embedding_layer = DistilBERT(input_ids = input_ids_in, attention_mask = input_masks_in)[0]\n",
    "X = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128, return_sequences=True))(embedding_layer)\n",
    "X = tf.keras.layers.GlobalMaxPool1D()(X)\n",
    "X = tf.keras.layers.Dense(64, activation='relu')(X)\n",
    "X = tf.keras.layers.Dropout(0.2)(X)\n",
    "X = tf.keras.layers.Dense(5, activation='softmax')(X)\n",
    "\n",
    "model = tf.keras.Model(inputs=[input_ids_in, input_masks_in], outputs = X)\n",
    "\n",
    "for layer in model.layers[:3]:\n",
    "    layer.trainable = False\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec73dae",
   "metadata": {},
   "source": [
    "*Creating a output directory to store the models weights*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "31c8f760",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create an output directory\n",
    "output_dir = './model1_outputs'\n",
    "if not os.path.exists(output_dir): ### If the file directory doesn't already exists,\n",
    "    os.makedirs(output_dir) ### Make it please"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f202c582",
   "metadata": {},
   "source": [
    "*Callbacks*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "69f6bb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_checkpoint = ModelCheckpoint(filepath=output_dir+'/weights.{epoch:02d}.hdf5',\n",
    "                                  save_weights_only=True)\n",
    "\n",
    "early_stopping = EarlyStopping(patience=3, # Stop after 3 epochs of no improvement\n",
    "                               monitor='val_loss', # Look at validation_loss\n",
    "                               min_delta=0, # After 0 change\n",
    "                               mode='min', # Stop when quantity has stopped decreasing\n",
    "                               restore_best_weights=False, # Don't Restore the best weights\n",
    "                               verbose=1) \n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', # Look at validation loss\n",
    "                              min_lr=0.000001, # Lower bound of learning rate\n",
    "                              patience=1, # Reduce after 1 with little change\n",
    "                              mode='min', # Stop when quantity has stopped decreasing\n",
    "                              factor=0.1, # Reduce by a factor of 1/10\n",
    "                              min_delta=0.01, # Minimumn change needed\n",
    "                              verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d65a407",
   "metadata": {},
   "source": [
    "**Compile and train**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aa02f04f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "2316/2316 [==============================] - ETA: 0s - loss: 1.3571 - accuracy: 0.4026WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "2316/2316 [==============================] - 588s 250ms/step - loss: 1.3570 - accuracy: 0.4026 - val_loss: 1.0693 - val_accuracy: 0.5474\n",
      "Epoch 2/10\n",
      "2316/2316 [==============================] - 576s 249ms/step - loss: 1.0445 - accuracy: 0.5642 - val_loss: 0.9584 - val_accuracy: 0.6178\n",
      "Epoch 3/10\n",
      "2316/2316 [==============================] - 574s 248ms/step - loss: 0.9503 - accuracy: 0.6111 - val_loss: 0.9372 - val_accuracy: 0.6110\n",
      "Epoch 4/10\n",
      "2316/2316 [==============================] - 575s 248ms/step - loss: 0.8734 - accuracy: 0.6451 - val_loss: 0.9340 - val_accuracy: 0.6370\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 5/10\n",
      "2316/2316 [==============================] - 574s 248ms/step - loss: 0.7637 - accuracy: 0.6975 - val_loss: 0.8732 - val_accuracy: 0.6579\n",
      "Epoch 6/10\n",
      "2316/2316 [==============================] - 577s 249ms/step - loss: 0.7168 - accuracy: 0.7183 - val_loss: 0.8726 - val_accuracy: 0.6545\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 7/10\n",
      "2316/2316 [==============================] - 580s 251ms/step - loss: 0.6984 - accuracy: 0.7281 - val_loss: 0.8737 - val_accuracy: 0.6535\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "Epoch 8/10\n",
      "2316/2316 [==============================] - 579s 250ms/step - loss: 0.6852 - accuracy: 0.7343 - val_loss: 0.8739 - val_accuracy: 0.6543\n",
      "\n",
      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
      "Epoch 9/10\n",
      "2316/2316 [==============================] - 577s 249ms/step - loss: 0.6858 - accuracy: 0.7358 - val_loss: 0.8739 - val_accuracy: 0.6543\n",
      "Epoch 00009: early stopping\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, \n",
    "                    y_train, \n",
    "                    epochs = 10,\n",
    "                    batch_size=16, \n",
    "                    validation_data=(X_val, y_val), \n",
    "                    callbacks=[model_checkpoint, early_stopping, reduce_lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124050cd",
   "metadata": {},
   "source": [
    "Plotting the training accuracy and loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e44380fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest Validation Loss: epoch 6\n",
      "Highest Validation Accuracy: epoch 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "    acc = history.history['accuracy']\n",
    "    val_acc = history.history['val_accuracy']\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    x = range(1, len(acc) + 1)\n",
    "\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(x, acc, 'b', label='Training acc')\n",
    "    plt.plot(x, val_acc, 'r', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.legend()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(x, loss, 'b', label='Training loss')\n",
    "    plt.plot(x, val_loss, 'r', label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.legend()\n",
    "    print(\"Lowest Validation Loss: epoch {}\".format(np.argmin(val_loss)+1))\n",
    "    print(\"Highest Validation Accuracy: epoch {}\".format(np.argmax(val_acc)+1))\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d81661",
   "metadata": {},
   "source": [
    "Loading the model with the best validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3e5f9e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_min_val_loss_epoch(history):\n",
    "    return \"0\"+str(np.argmin(history.history['val_loss'])+1)\n",
    "\n",
    "def get_max_val_acc_epoch(history):\n",
    "    return \"0\"+str(np.argmax(history.history['val_accuracy'])+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bb45c79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_num = get_max_val_acc_epoch(history)\n",
    "model.load_weights(output_dir+\"/weights.\"+epoch_num+\".hdf5\") # Load in model weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad1f61c",
   "metadata": {},
   "source": [
    "**Making inference on the test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aaf8917e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
     ]
    }
   ],
   "source": [
    "y_test_probs = model.predict(X_test)\n",
    "\n",
    "# Turn probabilities into an interger prediction\n",
    "y_hat = []\n",
    "for prob in y_test_probs:\n",
    "    y_hat.append(np.argmax(prob))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51c6ea7",
   "metadata": {},
   "source": [
    "**Results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e79eeca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6463928383359663\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test, y_hat))\n",
    "print_cf1(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dd8c05b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    precision    recall  f1-score   support\n",
      "\n",
      "Extremely Negative       0.71      0.66      0.68       592\n",
      "Extremely Positive       0.75      0.60      0.67       599\n",
      "          Negative       0.59      0.66      0.62      1041\n",
      "           Neutral       0.75      0.70      0.72       619\n",
      "          Positive       0.57      0.61      0.59       947\n",
      "\n",
      "          accuracy                           0.65      3798\n",
      "         macro avg       0.67      0.65      0.66      3798\n",
      "      weighted avg       0.66      0.65      0.65      3798\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat, target_names=labels_5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1225ab",
   "metadata": {},
   "source": [
    "## Combining Labels and Re-Running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e0b4872a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Negative\n",
      "1 Neutral\n",
      "2 Positive\n"
     ]
    }
   ],
   "source": [
    "def combining_labels(row):\n",
    "    if row in 'Extremely Positive':        \n",
    "        return 'Positive' \n",
    "    if row in 'Extremely Negative':\n",
    "        return 'Negative'\n",
    "    return row\n",
    "\n",
    "### Combining Labels\n",
    "train_df['Simple_Sentiment'] = train_df['Sentiment'].apply(combining_labels)\n",
    "test_df['Simple_Sentiment'] = test_df['Sentiment'].apply(combining_labels)\n",
    "\n",
    "### Encoding the labels\n",
    "le = LabelEncoder()\n",
    "train_df['Simple_Sentiment_label'] = le.fit_transform(train_df['Simple_Sentiment'])\n",
    "test_df['Simple_Sentiment_label'] = le.transform(test_df['Simple_Sentiment'])\n",
    "\n",
    "label_dict = (test_df[['Simple_Sentiment','Simple_Sentiment_label']].drop_duplicates()\n",
    "              .sort_values(by='Simple_Sentiment_label')\n",
    "              .reset_index(drop=True)['Simple_Sentiment']\n",
    "              .to_dict())\n",
    "\n",
    "for index, key in label_dict.items():\n",
    "    print(index, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1b2d28fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reorder_labels = [2,1,0]\n",
    "reorder = ['Positive', 'Neutral', 'Negative']\n",
    "\n",
    "fig, axes = plt.subplots(1,2 , figsize=(10,5))\n",
    "\n",
    "train_df.value_counts('Simple_Sentiment_label').reindex(index=reorder_labels).plot(kind='bar', ax=axes[0])\n",
    "plt.sca(axes[0])\n",
    "plt.xticks(reorder_labels, reorder, rotation=45, horizontalalignment='right')\n",
    "plt.title('Training Dataset')\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Label')\n",
    "\n",
    "test_df.value_counts('Simple_Sentiment_label').reindex(index=reorder_labels).plot(kind='bar', ax=axes[1])\n",
    "plt.sca(axes[1])\n",
    "plt.xticks(reorder_labels, reorder, rotation=45, horizontalalignment='right')\n",
    "plt.title('Testing Dataset')\n",
    "plt.xlabel('Label');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ad3e24",
   "metadata": {},
   "source": [
    "Splitting datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9c1bdd5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37041 training examples\n",
      "4116 validation examples\n",
      "3798 testing examples\n"
     ]
    }
   ],
   "source": [
    "### Only need to modify the labels\n",
    "y_train = train_df['Simple_Sentiment_label'][:num_train_samples]\n",
    "y_val = train_df['Simple_Sentiment_label'][num_train_samples:]\n",
    "y_test = test_df['Simple_Sentiment_label']\n",
    "\n",
    "print(f\"{len(y_train)} training examples\\n{len(y_val)} validation examples\")\n",
    "print(f\"{len(y_test)} testing examples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60439187",
   "metadata": {},
   "source": [
    "### Naive Bayes (Baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b9177dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_NB = train_df['Simple_Sentiment_label']\n",
    "y_test_NB = test_df['Simple_Sentiment_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9a9d0559",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
    "model.fit(X_train_NB, y_train_NB)\n",
    "\n",
    "### Make predictions\n",
    "y_hat = model.predict(X_test_NB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472d46a6",
   "metadata": {},
   "source": [
    "**Results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2241f468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6313849394418115\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test_NB, y_hat))\n",
    "labels_3 = ['Negative','Neutral','Positive'] \n",
    "\n",
    "def print_cf2(y_test, y_hat):\n",
    "    cm = confusion_matrix(y_test, y_hat)\n",
    "    sns.set(font_scale = 1.4, color_codes=True, palette=\"deep\")\n",
    "    sns.heatmap(pd.DataFrame(cm, index=labels_3,columns=[0,1,2]), \n",
    "                annot = True,\n",
    "                annot_kws = {\"size\":16},\n",
    "                fmt=\"d\",\n",
    "                cmap=\"YlGnBu\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.xlabel(\"Predicted Value\")\n",
    "    plt.xticks([0,1,2], labels_3, rotation=45)\n",
    "    plt.ylabel(\"True Value\")\n",
    "    plt.show()\n",
    "    \n",
    "print_cf2(y_test_NB, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5e5fa034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.76      0.62      0.68      1633\n",
      "     Neutral       0.75      0.00      0.01       619\n",
      "    Positive       0.56      0.89      0.69      1546\n",
      "\n",
      "    accuracy                           0.63      3798\n",
      "   macro avg       0.69      0.51      0.46      3798\n",
      "weighted avg       0.68      0.63      0.58      3798\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_NB, y_hat, target_names=labels_3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df8a48b",
   "metadata": {},
   "source": [
    "Our baseline to beat is **63% accuracy** in our predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80af2a33",
   "metadata": {},
   "source": [
    "## DistilBERT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7088a63",
   "metadata": {},
   "source": [
    "### Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2403b26f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_token (InputLayer)        [(None, 234)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "masked_token (InputLayer)       [(None, 234)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "tf_distil_bert_model_2 (TFDisti TFBaseModelOutput(la 65190912    input_token[0][0]                \n",
      "                                                                 masked_token[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 234, 256)     918528      tf_distil_bert_model_2[0][13]    \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_2 (GlobalM (None, 256)          0           bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 64)           16448       global_max_pooling1d_2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_59 (Dropout)            (None, 64)           0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 3)            195         dropout_59[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 66,126,083\n",
      "Trainable params: 935,171\n",
      "Non-trainable params: 65,190,912\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "config = DistilBertConfig.from_pretrained(MODEL_NAME, output_hidden_states=True, output_attentions=True, return_dict=True)\n",
    "DistilBERT = TFDistilBertModel.from_pretrained(MODEL_NAME, config=config)\n",
    "\n",
    "input_ids_in = tf.keras.layers.Input(shape=(MAX_LENGTH,), name='input_token', dtype='int32')\n",
    "attention_mask_in = tf.keras.layers.Input(shape=(MAX_LENGTH,), name='masked_token', dtype='int32') \n",
    "\n",
    "embedding_layer = DistilBERT(input_ids = input_ids_in, attention_mask = attention_mask_in)[0]\n",
    "X = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128, return_sequences=True))(embedding_layer)\n",
    "X = tf.keras.layers.GlobalMaxPool1D()(X)\n",
    "X = tf.keras.layers.Dense(64, activation='relu')(X)\n",
    "X = tf.keras.layers.Dropout(0.2)(X)\n",
    "X = tf.keras.layers.Dense(3, activation='softmax')(X)\n",
    "\n",
    "model = tf.keras.Model(inputs=[input_ids_in, attention_mask_in], outputs = X)\n",
    "\n",
    "for layer in model.layers[:3]:\n",
    "    layer.trainable = False\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73cba7b4",
   "metadata": {},
   "source": [
    "*Creating a new output director to store the models weights*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d5892bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create an output directory\n",
    "output_dir = './model2_outputs'\n",
    "if not os.path.exists(output_dir): ### If the file directory doesn't already exists,\n",
    "    os.makedirs(output_dir) ### Make it please"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3740f97",
   "metadata": {},
   "source": [
    "**Compile and train**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4ae48dc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "2315/2316 [============================>.] - ETA: 0s - loss: 0.8405 - accuracy: 0.6146WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
      "2316/2316 [==============================] - 584s 249ms/step - loss: 0.8404 - accuracy: 0.6146 - val_loss: 0.6429 - val_accuracy: 0.7257\n",
      "Epoch 2/10\n",
      "2316/2316 [==============================] - 573s 247ms/step - loss: 0.6204 - accuracy: 0.7489 - val_loss: 0.5740 - val_accuracy: 0.7672\n",
      "Epoch 3/10\n",
      "2316/2316 [==============================] - 573s 248ms/step - loss: 0.5433 - accuracy: 0.7871 - val_loss: 0.5487 - val_accuracy: 0.7845\n",
      "Epoch 4/10\n",
      "2316/2316 [==============================] - 572s 247ms/step - loss: 0.4776 - accuracy: 0.8140 - val_loss: 0.5372 - val_accuracy: 0.7884\n",
      "Epoch 5/10\n",
      "2316/2316 [==============================] - 562s 243ms/step - loss: 0.4314 - accuracy: 0.8370 - val_loss: 0.5380 - val_accuracy: 0.7886\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 6/10\n",
      "2316/2316 [==============================] - 560s 242ms/step - loss: 0.3564 - accuracy: 0.8704 - val_loss: 0.5517 - val_accuracy: 0.7952\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 7/10\n",
      "2316/2316 [==============================] - 560s 242ms/step - loss: 0.3144 - accuracy: 0.8854 - val_loss: 0.5557 - val_accuracy: 0.7952\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "Epoch 00007: early stopping\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, \n",
    "                    y_train, \n",
    "                    epochs = 10,\n",
    "                    batch_size=16, \n",
    "                    validation_data=(X_val, y_val), \n",
    "                    callbacks=[model_checkpoint, early_stopping, reduce_lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c1a840",
   "metadata": {},
   "source": [
    "Plotting the training accuracy and loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d8c5332b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest Validation Loss: epoch 4\n",
      "Highest Validation Accuracy: epoch 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c061f5",
   "metadata": {},
   "source": [
    "Loading the model weights with the best validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6976f32e",
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_num = get_max_val_acc_epoch(history)\n",
    "model.load_weights(output_dir+\"/weights.\"+epoch_num+\".hdf5\") # Load in model weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da397e6b",
   "metadata": {},
   "source": [
    "**Making Inference**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6c25a437",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
      "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
     ]
    }
   ],
   "source": [
    "y_test_probs = model.predict(X_test)\n",
    "\n",
    "# Turn probabilities into an interger prediction\n",
    "y_hat = []\n",
    "for prob in y_test_probs:\n",
    "    y_hat.append(np.argmax(prob))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04ff466",
   "metadata": {},
   "source": [
    "**Results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2177e936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7933122696155871\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy_score(y_test, y_hat))\n",
    "print_cf2(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1be55e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.81      0.81      0.81      1633\n",
      "     Neutral       0.76      0.71      0.73       619\n",
      "    Positive       0.79      0.81      0.80      1546\n",
      "\n",
      "    accuracy                           0.79      3798\n",
      "   macro avg       0.79      0.78      0.78      3798\n",
      "weighted avg       0.79      0.79      0.79      3798\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat, target_names=labels_3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f5b033c",
   "metadata": {},
   "source": [
    "### Looking at which tweets we guessed incorrectly "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "665141f0",
   "metadata": {},
   "source": [
    "Observations that were predicted **Negative** (y_hat = 0) but actually **Positive** (y_test = 2)\n",
    "\n",
    "*Printing the first 5 examples*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a7e4d75b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ok if #COVID2019 is nothing to panic about why is Italy imposing the biggest restrictions on the civilian population since WW2? \r",
      "\r\n",
      "How will the supermarkets be able to provide food if all the workers are told to stay at home? \r",
      "\r\n",
      "Same with any other Bussiness. \n",
      "\n",
      "IMPORTANT UPDATE! Our CEO Brian Beaulieu has broadened the scope of his COVID-19 webinar. Expect more on the oil prices drop, stock market developments, retail sales, and unfolding situations in China and Italy. Learn more and register:   \n",
      "\n",
      "Solid advice for seniors, their family members &amp; those of who have #complexhealth care needs.@CDC: Older #Americans should consider stocking up on food, medications &amp; avoid venturing out as #coronavirus spreads  #COVID19Canada #CoronavirusCanada #bcseniors \n",
      "\n",
      "As I appear to be absolutely no good at stock piling/panic buying  food goods and toilet rolls for the #coronavirus I'm moving on to something I'm good at buying ?\r",
      "\r\n",
      "\r",
      "\r\n",
      "Tin whistles and Apple Watch bands.\r",
      "\r\n",
      "\r",
      "\r\n",
      "Better than toilet rolls ?  \n",
      "\n",
      "How the fuck am I safer from #coronavirus on a train or at a gas station or at a grocery store or gym than at #sxsw or a ballgame or a amusement park? Either put guys with gas masks and guns on the streets or let us live our lives \n",
      "\n"
     ]
    }
   ],
   "source": [
    "[print(example, '\\n') for example in x_test[(y_test == 2) & (np.asarray(y_hat) == 0)][:5]];"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d05679c",
   "metadata": {},
   "source": [
    "Observations that were predicted **Positive** (y_hat = 2) but actually **Negative** (y_test = 0)\n",
    "\n",
    "*Printing the first 5 examples*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e4061c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#AirSewa \r",
      "\r\n",
      "\r",
      "\r\n",
      "@flyspicejet is not providing #webchecin customers free seats.\r",
      "\r\n",
      "\r",
      "\r\n",
      "Passengers are forced to choose paid seats as free seats are made available only at airport counter. \r",
      "\r\n",
      "\r",
      "\r\n",
      "This should be stopped as its discriminatory and against travellers rights \r",
      "\r\n",
      "\r",
      "\r\n",
      "@HardeepSPuri @MoCA_GoI \n",
      "\n",
      "Coronavirus panic spreads: Costco is pulling free samples over COVID-19 - USA TODAY\r",
      "\r\n",
      "\r",
      "\r\n",
      "Coronavirus panic spreads: Costco is pulling free samples over COVID-19  USA TODAY\r",
      "\r\n",
      "\r",
      "\r\n",
      "'It's pandemonium': virus panic-buying hits Los Angeles  Yahoo News\r",
      "\r\n",
      "\r",
      "\r\n",
      "How Costco is Ha",
      "  \n",
      "\n",
      "Is this effective?\r",
      "\r\n",
      "Is it necessary?\r",
      "\r\n",
      "Or is it just optics?\r",
      "\r\n",
      "Scare tactics?\r",
      "\r\n",
      "For the American consumer?\r",
      "\r\n",
      "For US MSM to use?\r",
      "\r\n",
      "If optics, What is the intended effect?\r",
      "\r\n",
      "Fear?\r",
      "\r\n",
      "Crash US Market-&gt;panic-&gt;elect Biden\r",
      "\r\n",
      "\r",
      "\r\n",
      "#Qanon #coronavirus  \n",
      "\n",
      "Fellow Uni instructors! COVID-19 f2f class cancelations are  inconvenient! But remember you're salaried employees. Maybe call/email HR or your union &amp; demand to know how hourly workers (clerical, food-service, custodial, etc) will be  compensated during campus closures? \n",
      "\n",
      "1/25 Part 1 - Why does soap work so well on the SARS-CoV-2, the coronavirus and indeed most viruses? Because it is a self-assembled nanoparticle in which the weakest link is the lipid (fatty) bilayer. A two part thread about soap, viruses and supramolecular chemistry #COVID19  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "[print(example, '\\n') for example in x_test[(y_test == 0) & (np.asarray(y_hat) == 2)][:5]];"
   ]
  }
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