Analise_Sentimentos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objetivo: Analise de sentimentos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leitura de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(748, 2)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>The incredible soundtrack truly captures the e...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>And those baby owls were adorable.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>I saw it as a child on TV back in 1973, when i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>Unless you're just out to visually \"collect\" a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>It handles some tough issues with dignity and ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>This is an extraordinary film.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>It's a long time since I was so entertained by...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>The film looks cheap and bland.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Very disappointing.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>If you see it, you should probably just leave ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                              sentence  label\n",
       "713  The incredible soundtrack truly captures the e...      1\n",
       "10                And those baby owls were adorable.        1\n",
       "584  I saw it as a child on TV back in 1973, when i...      1\n",
       "467  Unless you're just out to visually \"collect\" a...      0\n",
       "533  It handles some tough issues with dignity and ...      1\n",
       "591                   This is an extraordinary film.        1\n",
       "624  It's a long time since I was so entertained by...      1\n",
       "365                  The film looks cheap and bland.        0\n",
       "44                               Very disappointing.        0\n",
       "176  If you see it, you should probably just leave ...      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'dados_nlp/imdb_labelled.txt'\n",
    "\n",
    "df = pd.read_csv(path, names=['sentence', 'label'], sep='\\t')\n",
    "sentences = df['sentence'].values\n",
    "y = df['label'].values\n",
    "\n",
    "print (df.shape)\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separa dados em treino e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(561, 2505) (187, 2505)\n"
     ]
    }
   ],
   "source": [
    "sentences_train, sentences_test, y_train, y_test = train_test_split(\n",
    "                                                   sentences,\n",
    "                                                   y,\n",
    "                                                   test_size=0.25,\n",
    "                                                   random_state=1000)\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(sentences_train)\n",
    "\n",
    "X_train = vectorizer.transform(sentences_train)\n",
    "X_test  = vectorizer.transform(sentences_test)\n",
    "\n",
    "print (X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regressão logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7486631016042781\n"
     ]
    }
   ],
   "source": [
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train, y_train)\n",
    "score = classifier.score(X_test, y_test)\n",
    "print(\"Accuracy:\", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Árvore de decisão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6363636363636364\n"
     ]
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "score = classifier.score(X_test, y_test)\n",
    "print(\"Accuracy:\", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Redes neurais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 10)                25060     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 25,071\n",
      "Trainable params: 25,071\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Training Accuracy: 1.0000\n",
      "Accuracy:  0.7861\n"
     ]
    }
   ],
   "source": [
    "input_dim = X_train.shape[1]\n",
    "\n",
    "model = Sequential()\n",
    "model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', \n",
    "              optimizer='adam', \n",
    "              metrics=['accuracy'])\n",
    "model.summary()\n",
    "\n",
    "history = model.fit(X_train, y_train,\n",
    "                    epochs=100,\n",
    "                    verbose=False,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    batch_size=10)\n",
    "\n",
    "loss, accuracy = model.evaluate(X_train, y_train, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, y_test, verbose=False)\n",
    "print(\"Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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