Sentiment_Analysis_DNN.ipynb

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   "source": [
    "# Sentiment Analysis By DNN\n",
    "\n",
    "- Sentiment Analysis, or Opinion Mining, is a sub-field of Natural Language Processing (NLP) that tries to identify and extract opinions within a given text. \n",
    "\n",
    "- The aim of sentiment analysis is to gauge the attitude, sentiments, evaluations, attitudes and emotions of a speaker/writer based on the computational treatment of subjectivity in a text.\n",
    "\n",
    "- sentiment analysis so important Because Businesses today are heavily dependent on data. Majority of this data however, is unstructured text coming from sources like emails, chats, social media, surveys, articles, and documents. The micro-blogging content coming from Twitter and Facebook poses serious challenges, not only because of the amount of data involved, but also because of the kind of language used in them to express sentiments, i.e., short forms, memes and emoticons.\n",
    "\n",
    "- we use Imbd movie review dataset\n",
    "- spliting the dataset into train and test\n"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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    "outputId": "0b24e7a3-21d9-46ba-a884-e93c81d4f23f"
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     "text": [
      "Training Step: 7039  | total loss: \u001b[1m\u001b[32m0.04793\u001b[0m\u001b[0m | time: 127.291s\n",
      "| Adam | epoch: 010 | loss: 0.04793 - acc: 0.9877 -- iter: 22496/22500\n",
      "Training Step: 7040  | total loss: \u001b[1m\u001b[32m0.04376\u001b[0m\u001b[0m | time: 131.422s\n",
      "| Adam | epoch: 010 | loss: 0.04376 - acc: 0.9889 | val_loss: 0.90481 - val_acc: 0.7932 -- iter: 22500/22500\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tflearn\n",
    "from tflearn.data_utils import to_categorical, pad_sequences\n",
    "from tflearn.datasets import imdb\n",
    "\n",
    "# IMDB Dataset loading\n",
    "train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,\n",
    "                                valid_portion=0.1)\n",
    "trainX, trainY = train\n",
    "testX, testY = test\n",
    "\n",
    "# Data preprocessing\n",
    "# Sequence padding\n",
    "trainX = pad_sequences(trainX, maxlen=100, value=0.)\n",
    "testX = pad_sequences(testX, maxlen=100, value=0.)\n",
    "# Converting labels to binary vectors\n",
    "trainY = to_categorical(trainY, nb_classes=2)\n",
    "testY = to_categorical(testY, nb_classes=2)\n",
    "\n",
    "# Network building\n",
    "net = tflearn.input_data([None, 100])\n",
    "net = tflearn.embedding(net, input_dim=10000, output_dim=128)\n",
    "net = tflearn.lstm(net, 128, dropout=0.8)\n",
    "net = tflearn.fully_connected(net, 2, activation='softmax')\n",
    "net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,\n",
    "                         loss='categorical_crossentropy')\n",
    "\n",
    "# Training\n",
    "model = tflearn.DNN(net, tensorboard_verbose=0)\n",
    "model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True,\n",
    "          batch_size=32)"
   ]
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   "source": [
    "## Achiving the Accuracy of 98.89%"
   ]
  }
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