Movie Sentiment Analysis with GUI Application

This project performs sentiment analysis on IMDB movie reviews using Natural Language Processing (NLP) techniques and machine learning. It also includes a user-friendly GUI application built with Tkinter to analyze movie reviews interactively.

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Table of Contents

• Overview
• Features
• Technologies Used
• Dataset
• Installation
• Usage
  • Using the Script
  • Using the GUI Application
• Results

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Overview

This project uses machine learning models and NLP techniques to classify movie reviews as Positive or Negative based on their sentiment. The project includes:

1. A Python script for training and testing the models.
2. A GUI application for interactive sentiment analysis.

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Features

• Cleans and preprocesses text data using:
  • Stopword removal
  • Stemming with the PorterStemmer
• Visualizes dataset distributions with Matplotlib.
• Trains and evaluates multiple Naive Bayes classifiers:
  • MultinomialNB
  • GaussianNB
  • BernoulliNB
• GUI application built with Tkinter to allow real-time analysis of reviews.
• Models and vectorizers are serialized with Pickle for reusability.

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Technologies Used

• Programming Language: Python
• Libraries:
  • Pandas
  • NumPy
  • NLTK
  • Scikit-learn
  • Matplotlib
  • Pickle
  • Tkinter

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Dataset

The project uses the IMDB Dataset, which contains 50,000 labeled movie reviews:

• Columns:
  • review: The text of the movie review.
  • sentiment: The sentiment of the review (Positive/Negative).

Dataset Distribution

• Positive Reviews: 25,000
• Negative Reviews: 25,000

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Installation

Follow these steps to set up the project locally:

1. Clone the repository:

   git clone https://github.com/VedantSinghPundir/Movie-Sentimental-Analysis.git

2. Navigate to the project directory:

   cd movie-sentiment-analysis

3. Install the required dependencies:

   pip install -r requirements.txt

4. Download the IMDB dataset and save it in the project directory as IMDB Dataset.csv.

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Usage

Using the Script

1. Run the script to preprocess the data and train models:

   python movie_sentiment_analysis.py

2. Use the saved model to classify new reviews:

   from test_model import test_model
   
   # Example usage
   print(test_model("This is the best movie I have ever seen!"))  # Output: Positive review
   print(test_model("The movie was terrible and boring."))       # Output: Negative review

Using the GUI Application

1. Launch the GUI application :

   sentiment_analysis_app.py

2. Enter a movie review in the input box and click Analyze.

3. View the result (Positive/Negative sentiment) in a pop-up window.

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Results

The trained models achieved the following accuracy on the test set:

• MultinomialNB: 85.18%
• GaussianNB: 78.55%
• BernoulliNB (Best Model): 85.25%

Confusion Matrix (Best Model: BernoulliNB)

Weighted Precision:

• BernoulliNB: 85.27%

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