Sentiment Labels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./data/imdb_labelled.txt\", \"r\") as text_file:\n",
    "    lines = text_file.read().split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./data/amazon_cells_labelled.txt\", \"r\") as text_file:\n",
    "    lines += text_file.read().split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./data/yelp_labelled.txt\", \"r\") as text_file:\n",
    "    lines += text_file.read().split(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "newLines = [line.split(\"\\t\") for line in lines if len(line.split(\"\\t\")) == 2 and line.split(\"\\t\")[1] != '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#newLines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the newLines into Training Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_documents = [line[0] for line in newLines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the newLines into Training Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = [int(line[1]) for line in newLines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vectorizer = CountVectorizer(binary=\"true\")\n",
    "train_documents = count_vectorizer.fit_transform(train_documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2000x4100 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 21796 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Navie Bayes Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = BernoulliNB().fit(train_documents, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict if a sentence if +-ve or -ve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example 1\n",
    "classifier.predict(count_vectorizer.transform([\"This is the best movie ever\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example 2\n",
    "classifier.predict(count_vectorizer.transform(['very very bad worst day']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write a Python Function that outputs - 'This is a Positive Sentiment or This is a Negative Sentiment' \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentimentLabel(sentence):\n",
    "    prediction = classifier.predict(count_vectorizer.transform([sentence]))\n",
    "    if(prediction[0] == 1):\n",
    "        print(\"This is a Positive Sentiment Sentence\")\n",
    "    elif (prediction[0] == 0):\n",
    "        print(\"This is a Negative Sentiment Sentence\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Positive Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"I am having a very good and great day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Negative Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"I am very sick\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Negative Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"Today's weather is not that good to be out!!!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Positive Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"It's a great day to be watching a movie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Negative Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"I feeling ok\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Positive Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"I am feeling great!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Negative Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"I am feeling very sick\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a Negative Sentiment Sentence\n"
     ]
    }
   ],
   "source": [
    "sentimentLabel(\"I am not doing good\")"
   ]
  }
 ],
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