Analyze the sentiment of reviews with Keras.ipynb

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   "cell_type": "code",
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   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import imdb\n",
    "top_words = 10000\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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     "execution_count": 2,
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   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import sequence\n",
    "max_review_length = 500\n",
    "x_train = sequence.pad_sequences(x_train, maxlen=max_review_length)\n",
    "x_test = sequence.pad_sequences(x_test, maxlen=max_review_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 500, 32)           320000    \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 16000)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                256016    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 576,305\n",
      "Trainable params: 576,305\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers import Flatten\n",
    "\n",
    "embedding_vector_length = 32\n",
    "model = Sequential()\n",
    "model.add(Embedding(top_words, embedding_vector_length, input_length=max_review_length))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/5\n",
      "25000/25000 [==============================] - 12s 474us/step - loss: 0.5869 - acc: 0.6443 - val_loss: 0.3401 - val_acc: 0.8494\n",
      "Epoch 2/5\n",
      "25000/25000 [==============================] - 12s 463us/step - loss: 0.2266 - acc: 0.9108 - val_loss: 0.2757 - val_acc: 0.8843\n",
      "Epoch 3/5\n",
      "25000/25000 [==============================] - 12s 464us/step - loss: 0.0949 - acc: 0.9720 - val_loss: 0.3245 - val_acc: 0.8783\n",
      "Epoch 4/5\n",
      "25000/25000 [==============================] - 11s 434us/step - loss: 0.0254 - acc: 0.9952 - val_loss: 0.4254 - val_acc: 0.8679\n",
      "Epoch 5/5\n",
      "25000/25000 [==============================] - 12s 489us/step - loss: 0.0067 - acc: 0.9990 - val_loss: 0.4797 - val_acc: 0.8693\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.93%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/text-datasets/imdb_word_index.json\n",
      "1646592/1641221 [==============================] - 0s 0us/step\n",
      "                                                                                                                                                                                                                                                                                          > this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n"
     ]
    }
   ],
   "source": [
    "word_dict = imdb.get_word_index()\n",
    "word_dict = { key:(value + 3) for key, value in word_dict.items() }\n",
    "word_dict[''] = 0  # Padding\n",
    "word_dict['>'] = 1 # Start\n",
    "word_dict['?'] = 2 # Unknown word\n",
    "reverse_word_dict = { value:key for key, value in word_dict.items() }\n",
    "print(' '.join(reverse_word_dict[id] for id in x_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import numpy as np\n",
    "\n",
    "def analyze(text):\n",
    "    # Removed punctuation characters, converting\n",
    "    # characters to lower case, and removing words containing numbers\n",
    "    translator = str.maketrans('', '', string.punctuation)\n",
    "    text = text.translate(translator)\n",
    "    text = text.lower().split(' ')\n",
    "    text = [word for word in text if word.isalpha()]\n",
    "    input = [1]\n",
    "    for word in text:\n",
    "        if word in word_dict and word_dict[word] < top_words:\n",
    "            input.append(word_dict[word])\n",
    "        else:\n",
    "            input.append(2)\n",
    "    padded_input = sequence.pad_sequences([input], maxlen=max_review_length)\n",
    "    result = model.predict(np.array([padded_input][0]))[0][0]\n",
    "    return result\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
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     "execution_count": 14,
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   ],
   "source": [
    "analyze('Easily the most stellar experience I have ever had.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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