Sentiment_Analysis_using_Bag_of_Words_Model_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sentiment Analysis using Bag of Words Model .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AVIRALAKASH/Sentiment-Analysis-of-Restaurant-Reviews/blob/main/Sentiment_Analysis_using_Bag_of_Words_Model_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1kiO9kACE6s"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QG7sxmoCIvN"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTfaCIzdCLPA"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCK6vQ5QCQJe"
      },
      "source": [
        "dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\\t', quoting = 3)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qekztq71CixT"
      },
      "source": [
        "## Cleaning the texts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u_yXh9dCmEE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b540435-561f-4f5f-9c37-836f38b5f9e5"
      },
      "source": [
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "corpus = []\n",
        "for i in range(0, 1000):\n",
        "  review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])\n",
        "  review = review.lower()\n",
        "  review = review.split()\n",
        "  ps = PorterStemmer()\n",
        "  all_stopwords = stopwords.words('english')\n",
        "  all_stopwords.remove('not')\n",
        "  review = [ps.stem(word) for word in review if not word in set(all_stopwords)]\n",
        "  review = ' '.join(review)\n",
        "  corpus.append(review)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLqmAkANCp1-"
      },
      "source": [
        "## Creating the Bag of Words model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qroF7XcSCvY3"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features = 1500)\n",
        "X = cv.fit_transform(corpus).toarray()\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VkIq23vEDIPt"
      },
      "source": [
        "## Training the Naive Bayes model on the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DS9oiDXXDRdI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afd80bbf-5ac3-4f5f-e001-a2f81382be69"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X,y)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZcOJH_x9GSV"
      },
      "source": [
        "## Constructing function for a single review"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySAblyR98J2s"
      },
      "source": [
        "def POSorNEG(a):\n",
        "  a = re.sub('[^a-zA-Z]', ' ',a)\n",
        "  a = a.lower()\n",
        "  a = a.split()\n",
        "  ps = PorterStemmer()\n",
        "  all_stopwords = stopwords.words('english')\n",
        "  all_stopwords.remove('not')\n",
        "  a = [ps.stem(word) for word in a if not word in set(all_stopwords)]\n",
        "  a = ' '.join(a)\n",
        "  new_corpus = [a]\n",
        "  new_X_test = cv.transform(new_corpus).toarray()\n",
        "  new_y_pred = classifier.predict(new_X_test)\n",
        "  if new_y_pred==[1]:\n",
        "    return \"The Review is Positive\"\n",
        "  else:\n",
        "    return \"The Review is Negative\"\n"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmhV3AjixyZ9"
      },
      "source": [
        "# **ENTER YOUR REVIEW HERE:-**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "xc1YcXLwvnix",
        "outputId": "dcc5f636-3a5f-46fb-fd58-d40f7de88b5a"
      },
      "source": [
        "YOUR_REVIEW=input(\"Enter your Review:  \")\n",
        "POSorNEG(YOUR_REVIEW)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter your Review:  the food was fresh\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The Review is Positive'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    }
  ]
}