Emoji_DL_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emoji Prediction using Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "KefB9sl0AuNP"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "YU0-punLAuNU"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/stp_lm.csv\")\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "E3EAr6bMAuNY",
    "outputId": "7f016950-c8a6-4480-819d-1dc3510fcbae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>little throwback favourite person   water wall</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>glam   yesterday kcon makeup   featherette</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>democracy plaza wake stunning outcome decision...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amp vilo   walt disney magic kingdom</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>galaxy far far away</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dinner fla tonight   panseared salmon couscous...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fav senior game congrats beating west   west s...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>got formal best friend   phi mu jsu</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cause miss little homies throwback cousinlove ...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>birthday kiss   madison wisconsin</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Label\n",
       "0     little throwback favourite person   water wall      0\n",
       "1         glam   yesterday kcon makeup   featherette      7\n",
       "2  democracy plaza wake stunning outcome decision...     11\n",
       "3               amp vilo   walt disney magic kingdom      0\n",
       "4                                galaxy far far away      2\n",
       "5  dinner fla tonight   panseared salmon couscous...      1\n",
       "6  fav senior game congrats beating west   west s...      8\n",
       "7                got formal best friend   phi mu jsu      0\n",
       "8  cause miss little homies throwback cousinlove ...     13\n",
       "9                  birthday kiss   madison wisconsin      9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EuJWwArsAuNd"
   },
   "source": [
    "### Over-sampling data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "6fAkJ1_1AuNe"
   },
   "outputs": [],
   "source": [
    "X = df[\"Tweet\"].values\n",
    "y = df[\"Label\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zvJvAx1Zzk2c",
    "outputId": "7a2c2575-8fb7-4019-ab0f-5b0c3a54f9a0"
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "os = RandomOverSampler()\n",
    "X = X.reshape(-1,1)\n",
    "X, y = os.fit_sample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qjFsaJEU2SCy",
    "outputId": "aaac7ee7-da1d-4a04-c350-979748cbafde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49985,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.reshape(-1,)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BB0oBw-eAuNi"
   },
   "source": [
    "#   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dXWMjTNYAuNj"
   },
   "source": [
    "### Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "65z0pXN_AuNk"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "\n",
    "tokenizer = Tokenizer(num_words=80000)\n",
    "tokenizer.fit_on_texts(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VDWxkBNLAuNn",
    "outputId": "8a8fd003-7102-42b9-87d6-ff44703a0eb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text \n",
      " ['little throwback favourite person   water wall'\n",
      " 'glam   yesterday kcon makeup   featherette'\n",
      " 'democracy plaza wake stunning outcome decision   nbc news'\n",
      " 'amp vilo   walt disney magic kingdom' '  galaxy far far away'\n",
      " 'dinner fla tonight   panseared salmon couscous veggie salad yum dinner florida salmon'\n",
      " 'fav senior game congrats beating west   west salem'\n",
      " 'got formal best friend   phi mu jsu'\n",
      " 'cause miss little homies throwback cousinlove   indiana university'\n",
      " 'birthday kiss   madison wisconsin'] \n",
      "\n",
      "Sequences\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[25, 310, 1605, 288, 474, 622],\n",
       " [2748, 194, 15729, 526, 15730],\n",
       " [5764, 623, 898, 974, 7993, 2906, 4598, 1205],\n",
       " [6, 15731, 372, 57, 149, 228],\n",
       " [2225, 639, 639, 409],\n",
       " [106, 4599, 40, 15732, 2140, 15733, 3118, 1301, 1206, 106, 51, 2140],\n",
       " [373, 321, 94, 374, 6679, 127, 127, 2335],\n",
       " [16, 870, 13, 17, 788, 2907, 7994],\n",
       " [380, 65, 25, 1562, 310, 4167, 402, 21],\n",
       " [22, 624, 496, 687]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Text \\n\",X[:10],'\\n')\n",
    "\n",
    "X_seqs = tokenizer.texts_to_sequences(X)\n",
    "print(\"Sequences\")\n",
    "X_seqs[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wq8BAJxrAuNr",
    "outputId": "1cfc2054-4d8c-42f7-e030-7940c54820b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word Mappings\n",
      "\n",
      "Index:  1 ----> love\n",
      "Index:  2 ----> new\n",
      "Index:  3 ----> day\n",
      "Index:  4 ----> nt\n",
      "Index:  5 ----> happy\n",
      "Index:  6 ----> amp\n",
      "Index:  7 ----> m\n",
      "Index:  8 ----> time\n",
      "Index:  9 ----> night\n"
     ]
    }
   ],
   "source": [
    "idx2words = tokenizer.index_word\n",
    "\n",
    "print(\"Word Mappings\\n\")\n",
    "for i in range(1,10):\n",
    "    print(\"Index: \",i, \"---->\",idx2words[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vb8OcW7rAuNv",
    "outputId": "7bf04ea4-d540-41fe-90fc-b5fb5bf47269"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size : 54697\n"
     ]
    }
   ],
   "source": [
    "vocab_size = len(tokenizer.index_word)\n",
    "print(\"Vocabulary size :\",len(tokenizer.index_word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "AkAUEaoCAuN1",
    "outputId": "82482c4f-da7a-42a2-d2c5-430e9aba2284"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lens = [len(seq) for seq in X_seqs]\n",
    "sns.countplot(x=lens)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "MzGJnOcvAuN5"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "max_len=40\n",
    "X_seqs_pd = pad_sequences(X_seqs, truncating=\"pre\", padding=\"pre\", maxlen=max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Padding\n",
      "[[25, 310, 1605, 288, 474, 622]]\n",
      "\n",
      "After Padding\n",
      "[[   0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "     0    0    0    0    0    0   25  310 1605  288  474  622]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Before Padding\")\n",
    "print(X_seqs[:1])\n",
    "\n",
    "print(\"\\nAfter Padding\")\n",
    "print(X_seqs_pd[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "zGlQpu8KAuN9",
    "outputId": "150d7587-b12d-4986-cbfd-883f1e0d93a9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lens = [len(seq) for seq in X_seqs_pd]\n",
    "sns.countplot(x=lens)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mYwu-d43M_ch",
    "outputId": "a99bed8f-e1a1-444c-e1f6-72f187b25ba4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training X Dim  (150626, 40)\n",
      "Training y Dim  (150626, 20)\n",
      "Test X Dim  (64554, 40)\n",
      "Test y Dim  (64554, 20)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test ,y_train, y_test = train_test_split(X_seqs_pd, y, test_size=0.3, random_state=105)\n",
    "print(\"Training X Dim \",X_train.shape)\n",
    "print(\"Training y Dim \",y_train.shape)\n",
    "\n",
    "print(\"Test X Dim \",X_test.shape)\n",
    "print(\"Test y Dim \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gyy2IgPvAuOA"
   },
   "source": [
    "#   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gyy2IgPvAuOA"
   },
   "source": [
    "## Deep Learning  Model using LSTMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9oXcw9wqAuOB",
    "outputId": "0b9d9e23-9700-493d-bff3-10c50a3a34ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 40, 20)            1253440   \n",
      "_________________________________________________________________\n",
      "lstm_6 (LSTM)                (None, 40, 80)            32320     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 40, 80)            0         \n",
      "_________________________________________________________________\n",
      "lstm_7 (LSTM)                (None, 40)                19360     \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 20)                820       \n",
      "=================================================================\n",
      "Total params: 1,305,940\n",
      "Trainable params: 1,305,940\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout, Dense, LSTM, Bidirectional,Embedding, GlobalMaxPool1D\n",
    "\n",
    "\n",
    "\n",
    "vocabulary_size = vocab_size\n",
    "seq_len = 40\n",
    "embed_len=20\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(vocabulary_size+1, embed_len, input_length=seq_len))\n",
    "model.add(LSTM(units = 80, return_sequences=True))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(LSTM(units = 40))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=20, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "xbzYqIY_QWGn"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "early_stop = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=1,verbose=1)\n",
    "\n",
    "model.compile(loss = 'categorical_crossentropy',\n",
    "             optimizer = \"adam\",\n",
    "             metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "y_train_cat = to_categorical(y_train)\n",
    "y_test_cat = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vRdqCMbGAuOI",
    "outputId": "ca799c41-0d78-4fb2-daaa-4c1b5ca36335"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1177/1177 [==============================] - 25s 21ms/step - loss: 2.4237 - accuracy: 0.2475 - val_loss: 1.7973 - val_accuracy: 0.4525\n",
      "Epoch 2/20\n",
      "1177/1177 [==============================] - 24s 20ms/step - loss: 1.4758 - accuracy: 0.5607 - val_loss: 1.1806 - val_accuracy: 0.6666\n",
      "Epoch 3/20\n",
      "1177/1177 [==============================] - 24s 20ms/step - loss: 0.9992 - accuracy: 0.7178 - val_loss: 0.9039 - val_accuracy: 0.7511\n",
      "Epoch 4/20\n",
      "1177/1177 [==============================] - 24s 21ms/step - loss: 0.7420 - accuracy: 0.7943 - val_loss: 0.7573 - val_accuracy: 0.7995\n",
      "Epoch 5/20\n",
      "1177/1177 [==============================] - 24s 21ms/step - loss: 0.5991 - accuracy: 0.8347 - val_loss: 0.7019 - val_accuracy: 0.8205\n",
      "Epoch 6/20\n",
      "1177/1177 [==============================] - 24s 20ms/step - loss: 0.5062 - accuracy: 0.8620 - val_loss: 0.6547 - val_accuracy: 0.8379\n",
      "Epoch 7/20\n",
      "1177/1177 [==============================] - 24s 20ms/step - loss: 0.4369 - accuracy: 0.8817 - val_loss: 0.6341 - val_accuracy: 0.8493\n",
      "Epoch 8/20\n",
      "1177/1177 [==============================] - 24s 20ms/step - loss: 0.3876 - accuracy: 0.8950 - val_loss: 0.6074 - val_accuracy: 0.8619\n",
      "Epoch 9/20\n",
      "1177/1177 [==============================] - 24s 20ms/step - loss: 0.3423 - accuracy: 0.9080 - val_loss: 0.6077 - val_accuracy: 0.8668\n",
      "Epoch 00009: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7ff1ac36b748>"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "batch_size=128\n",
    "epochs = 20\n",
    "\n",
    "model.fit(X_train, y_train_cat, batch_size=batch_size, epochs = epochs, shuffle=True,\n",
    "          validation_data=(X_test, y_test_cat), callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tvDYrJ4cG4Do"
   },
   "outputs": [],
   "source": [
    "model.evaluate(x_test, y_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "R6QQrHTZcXPO",
    "outputId": "ad4d5511-49b5-4350-822e-ee653dfa68ce"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.423696</td>\n",
       "      <td>0.247467</td>\n",
       "      <td>1.797268</td>\n",
       "      <td>0.452458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.475779</td>\n",
       "      <td>0.560713</td>\n",
       "      <td>1.180616</td>\n",
       "      <td>0.666558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.999234</td>\n",
       "      <td>0.717778</td>\n",
       "      <td>0.903867</td>\n",
       "      <td>0.751139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.742029</td>\n",
       "      <td>0.794292</td>\n",
       "      <td>0.757338</td>\n",
       "      <td>0.799455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.599075</td>\n",
       "      <td>0.834730</td>\n",
       "      <td>0.701901</td>\n",
       "      <td>0.820538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.506225</td>\n",
       "      <td>0.861976</td>\n",
       "      <td>0.654693</td>\n",
       "      <td>0.837888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.436892</td>\n",
       "      <td>0.881694</td>\n",
       "      <td>0.634060</td>\n",
       "      <td>0.849258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.387615</td>\n",
       "      <td>0.894952</td>\n",
       "      <td>0.607426</td>\n",
       "      <td>0.861868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.342276</td>\n",
       "      <td>0.907991</td>\n",
       "      <td>0.607691</td>\n",
       "      <td>0.866825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  2.423696  0.247467  1.797268      0.452458\n",
       "1  1.475779  0.560713  1.180616      0.666558\n",
       "2  0.999234  0.717778  0.903867      0.751139\n",
       "3  0.742029  0.794292  0.757338      0.799455\n",
       "4  0.599075  0.834730  0.701901      0.820538\n",
       "5  0.506225  0.861976  0.654693      0.837888\n",
       "6  0.436892  0.881694  0.634060      0.849258\n",
       "7  0.387615  0.894952  0.607426      0.861868\n",
       "8  0.342276  0.907991  0.607691      0.866825"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = pd.DataFrame(model.history.history)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "H70ZcHPgcaU4",
    "outputId": "22e2843b-7caf-4f0f-803a-14fb60d62e3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fef618b8a58>,\n",
       " <matplotlib.lines.Line2D at 0x7fef618b8b70>,\n",
       " <matplotlib.lines.Line2D at 0x7fef618b8cc0>,\n",
       " <matplotlib.lines.Line2D at 0x7fef618b8e10>]"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning  Model using Bidirectional-LSTMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9oXcw9wqAuOB",
    "outputId": "7376b5cf-da42-47c4-b298-1c43aa1d6bf9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 40, 128)           8022016   \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 40, 160)           133760    \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 40, 160)           154240    \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d (Global (None, 160)               0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 160)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                10304     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 20)                1300      \n",
      "=================================================================\n",
      "Total params: 8,321,620\n",
      "Trainable params: 8,321,620\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout, Dense, LSTM, Bidirectional,Embedding, GlobalMaxPool1D\n",
    "\n",
    "\n",
    "\n",
    "vocabulary_size = vocab_size\n",
    "seq_len = 40\n",
    "embed_len=128\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(vocabulary_size+1, embed_len, input_length=seq_len))\n",
    "model.add(Bidirectional(LSTM(80,return_sequences=True)))\n",
    "model.add(Bidirectional(LSTM(80,return_sequences=True)))\n",
    "model.add(GlobalMaxPool1D())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(20, activation=\"softmax\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "xbzYqIY_QWGn"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "early_stop = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=0,verbose=1)\n",
    "\n",
    "model.compile(loss = 'categorical_crossentropy',\n",
    "             optimizer = \"adam\",\n",
    "             metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "y_train_cat = to_categorical(y_train)\n",
    "y_test_cat = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 649
    },
    "id": "vRdqCMbGAuOI",
    "outputId": "0c196338-abc7-4594-a1d7-69a319d57697"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "2354/2354 [==============================] - 224s 95ms/step - loss: 2.2879 - accuracy: 0.2940 - val_loss: 1.4041 - val_accuracy: 0.5763\n",
      "Epoch 2/15\n",
      "2354/2354 [==============================] - 223s 95ms/step - loss: 1.0589 - accuracy: 0.6898 - val_loss: 0.8214 - val_accuracy: 0.7561\n",
      "Epoch 3/15\n",
      "2354/2354 [==============================] - 223s 95ms/step - loss: 0.6128 - accuracy: 0.8237 - val_loss: 0.6235 - val_accuracy: 0.8254\n",
      "Epoch 4/15\n",
      "2354/2354 [==============================] - 223s 95ms/step - loss: 0.4133 - accuracy: 0.8837 - val_loss: 0.5428 - val_accuracy: 0.8616\n",
      "Epoch 5/15\n",
      "2354/2354 [==============================] - 222s 94ms/step - loss: 0.3023 - accuracy: 0.9167 - val_loss: 0.4961 - val_accuracy: 0.8795\n",
      "Epoch 6/15\n",
      "2354/2354 [==============================] - 222s 94ms/step - loss: 0.2319 - accuracy: 0.9374 - val_loss: 0.5038 - val_accuracy: 0.8874\n",
      "Epoch 00006: early stopping\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "batch_size=64\n",
    "epochs = 15\n",
    "\n",
    "model.fit(X_train, y_train, batch_size=batch_size, epochs = epochs, \n",
    "          shuffle=True,validation_data=(X_test, y_test), callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tvDYrJ4cG4Do",
    "outputId": "17558846-5d24-4c1b-b270-31070c93ed28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1009/1009 [==============================] - 7s 7ms/step - loss: 0.5038 - accuracy: 0.8874\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5403534173965454, 0.8935310244560242]"
      ]
     },
     "execution_count": 83,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "R6QQrHTZcXPO",
    "outputId": "d61fd7bf-67e7-4ac1-f905-c8b096982377"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.2879</td>\n",
       "      <td>0.2940</td>\n",
       "      <td>1.4041</td>\n",
       "      <td>0.5763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0589</td>\n",
       "      <td>0.6898</td>\n",
       "      <td>0.8214</td>\n",
       "      <td>0.7561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6128</td>\n",
       "      <td>0.8237</td>\n",
       "      <td>0.6235</td>\n",
       "      <td>0.8254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4133</td>\n",
       "      <td>0.8837</td>\n",
       "      <td>0.5428</td>\n",
       "      <td>0.8616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3023</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>0.4961</td>\n",
       "      <td>0.8795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2319</td>\n",
       "      <td>0.9374</td>\n",
       "      <td>0.5038</td>\n",
       "      <td>0.8874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  2.2879  0.2940  1.4041      0.5763\n",
       "1  1.0589  0.6898  0.8214      0.7561\n",
       "2  0.6128  0.8237  0.6235      0.8254\n",
       "3  0.4133  0.8837  0.5428      0.8616\n",
       "4  0.3023  0.9167  0.4961      0.8795\n",
       "5  0.2319  0.9374  0.5038      0.8874\n"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = pd.DataFrame(model.history.history)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "H70ZcHPgcaU4",
    "outputId": "94475b6f-6a98-499c-8314-1c1a53d2a48b"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss[[\"loss\",\"val_loss\"]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "lx0BaS3xOY4K",
    "outputId": "0860f00d-ac3d-4aff-e4ef-69170093a504"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffb1188f160>,\n",
       " <matplotlib.lines.Line2D at 0x7ffb1188f278>]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss[[\"accuracy\",\"val_accuracy\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7nP_mKV-ZXei"
   },
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ttl-REfhZvDq",
    "outputId": "8bf98a7d-17f4-42ac-f7f3-e9652fcea66c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.31      0.40      3211\n",
      "           1       0.69      0.64      0.66      3249\n",
      "           2       0.84      0.73      0.78      3240\n",
      "           3       0.79      0.85      0.82      3175\n",
      "           4       0.93      0.93      0.93      3208\n",
      "           5       0.82      0.88      0.85      3281\n",
      "           6       0.89      0.93      0.91      3255\n",
      "           7       0.87      0.94      0.90      3172\n",
      "           8       0.93      0.93      0.93      3273\n",
      "           9       0.84      0.94      0.89      3226\n",
      "          10       0.96      0.89      0.92      3202\n",
      "          11       0.93      0.98      0.95      3216\n",
      "          12       0.95      0.97      0.96      3277\n",
      "          13       0.95      0.97      0.96      3202\n",
      "          14       0.95      0.97      0.96      3225\n",
      "          15       0.95      0.98      0.97      3177\n",
      "          16       0.94      0.96      0.95      3234\n",
      "          17       0.97      0.98      0.97      3224\n",
      "          18       0.87      0.99      0.92      3235\n",
      "          19       0.96      0.97      0.96      3272\n",
      "\n",
      "    accuracy                           0.89     64554\n",
      "   macro avg       0.88      0.89      0.88     64554\n",
      "weighted avg       0.88      0.89      0.88     64554\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "\n",
    "predictions = model.predict_classes(X_test)\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 863
    },
    "id": "Z90F94iEZ-2i",
    "outputId": "7737530a-6733-447e-e3dd-17c7493a2c7f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "mat = confusion_matrix(y_test, predictions)\n",
    "sns.heatmap(mat, annot=True)                                                                   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "bdyri7QeaSHC"
   },
   "outputs": [],
   "source": [
    "model.save(\"BLSTM.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy=[0.27,0.29,0.85,0.89]\n",
    "labels=['Naive Bayes','Support Vector Classifier','LSTM','Bidirectional LSTM']\n",
    "\n",
    "\n",
    "sns.set(rc={'figure.figsize':(8,6)})\n",
    "ax = sns.barplot(x=labels, y=accuracy)\n",
    "ax.set(xlabel='Model', ylabel='Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Emoji_DL_90BL.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}