sentiment_lstm.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5b2497b3-60ee-7cd0-0625-f103214c0ed4"
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   "source": [
    "**Sentiment Analysis:** the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "6c53202d-5c34-4859-e7e9-8ef5c7068287"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, LSTM\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "89c8c923-c0bf-7b35-9ab8-e63f00b74e5a"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('../input/Sentiment.csv')\n",
    "data = data[['text','sentiment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "43632d2d-6160-12ce-48b0-e5eb1c207076"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4472\n",
      "16986\n"
     ]
    }
   ],
   "source": [
    "data = data[data.sentiment != \"Neutral\"]\n",
    "data['text'] = data['text'].apply(lambda x: x.lower())\n",
    "data['text'] = data['text'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x)))\n",
    "\n",
    "print(data[ data['sentiment'] == 'Positive'].size)\n",
    "print(data[ data['sentiment'] == 'Negative'].size)\n",
    "\n",
    "for idx,row in data.iterrows():\n",
    "    row[0] = row[0].replace('rt',' ')\n",
    "    \n",
    "max_fatures = 2000\n",
    "tokenizer = Tokenizer(nb_words=max_fatures, split=' ')\n",
    "tokenizer.fit_on_texts(data['text'].values)\n",
    "X = tokenizer.texts_to_sequences(data['text'].values)\n",
    "X = pad_sequences(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "1ba3cf60-a83c-9c21-05e0-b14303027e93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "embedding_1 (Embedding)          (None, 28, 128)       256000      embedding_input_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                    (None, 196)           254800      embedding_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 2)             394         lstm_1[0][0]                     \n",
      "====================================================================================================\n",
      "Total params: 511,194\n",
      "Trainable params: 511,194\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "embed_dim = 128\n",
    "lstm_out = 196\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(max_fatures, embed_dim,input_length = X.shape[1], dropout=0.2))\n",
    "model.add(LSTM(lstm_out, dropout_U=0.2, dropout_W=0.2))\n",
    "model.add(Dense(2,activation='softmax'))\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "b35748b8-2353-3db2-e571-5fd22bb93eb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7188, 28) (7188, 2)\n",
      "(3541, 28) (3541, 2)\n"
     ]
    }
   ],
   "source": [
    "Y = pd.get_dummies(data['sentiment']).values\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.33, random_state = 42)\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "d5e499ac-2eba-6ff7-8d9a-ff65eb04099b"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:91: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "57s - loss: 0.4678 - acc: 0.8066\n",
      "Epoch 2/7\n",
      "56s - loss: 0.3480 - acc: 0.8529\n",
      "Epoch 3/7\n",
      "55s - loss: 0.2970 - acc: 0.8740\n",
      "Epoch 4/7\n",
      "56s - loss: 0.2718 - acc: 0.8848\n",
      "Epoch 5/7\n",
      "56s - loss: 0.2459 - acc: 0.8965\n",
      "Epoch 6/7\n",
      "56s - loss: 0.2309 - acc: 0.9008\n",
      "Epoch 7/7\n",
      "56s - loss: 0.2129 - acc: 0.9060\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f535974b898>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 32\n",
    "model.fit(X_train, Y_train, nb_epoch = 7, batch_size=batch_size, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "a970f412-722f-6d6d-72c8-325d0901ccef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.42\n",
      "acc: 0.84\n"
     ]
    }
   ],
   "source": [
    "validation_size = 1500\n",
    "\n",
    "X_validate = X_test[-validation_size:]\n",
    "Y_validate = Y_test[-validation_size:]\n",
    "X_test = X_test[:-validation_size]\n",
    "Y_test = Y_test[:-validation_size]\n",
    "score,acc = model.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)\n",
    "print(\"score: %.2f\" % (score))\n",
    "print(\"acc: %.2f\" % (acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "1add73e9-c6fb-7e4c-8715-ea92f519d2a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos_acc 56.63430420711975 %\n",
      "neg_acc 92.7791771620487 %\n"
     ]
    }
   ],
   "source": [
    "pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0\n",
    "for x in range(len(X_validate)):\n",
    "    \n",
    "    result = model.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]\n",
    "   \n",
    "    if np.argmax(result) == np.argmax(Y_validate[x]):\n",
    "        if np.argmax(Y_validate[x]) == 0:\n",
    "            neg_correct += 1\n",
    "        else:\n",
    "            pos_correct += 1\n",
    "       \n",
    "    if np.argmax(Y_validate[x]) == 0:\n",
    "        neg_cnt += 1\n",
    "    else:\n",
    "        pos_cnt += 1\n",
    "\n",
    "\n",
    "\n",
    "print(\"pos_acc\", pos_correct/pos_cnt*100, \"%\")\n",
    "print(\"neg_acc\", neg_correct/neg_cnt*100, \"%\")"
   ]
  }
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