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https://github.com/rahul-sahani04/moviereviewsentimentanalysis

This project utilizes web scraping, sentiment analysis, and machine learning to analyze IMDb movie reviews and provide insights into the sentiment of the audience. The sentiment analysis is performed using the VADER sentiment analysis tool, and the results are visualized through a Streamlit app. Users can input an IMDb code to get a quick overview.
https://github.com/rahul-sahani04/moviereviewsentimentanalysis

machine-learning python3 sentiment-analysis streamlit

Last synced: about 1 month ago
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Host: GitHub
URL: https://github.com/rahul-sahani04/moviereviewsentimentanalysis
Owner: Rahul-Sahani04
Created: 2023-12-30T07:37:43.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-01-05T14:59:47.000Z (about 1 year ago)
Last Synced: 2024-11-08T07:42:10.575Z (3 months ago)
Topics: machine-learning, python3, sentiment-analysis, streamlit
Language: Jupyter Notebook
Homepage: https://moviereviewsentimentanalysis.streamlit.app/
Size: 1.27 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: Readme.md

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README

# Movie Review Sentiment Analysis

## Overview

This project focuses on sentiment analysis of movie reviews extracted from IMDb. The sentiment analysis is performed using a combination of web scraping, natural language processing (NLP) techniques, and machine learning algorithms. The goal is to determine the sentiment (positive or negative) of each movie review.

## Table of Contents

- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Project Structure](#project-structure)
- [Web Scraping](#web-scraping)
- [Data Preprocessing](#data-preprocessing)
- [Sentiment Analysis](#sentiment-analysis)
- [Model Evaluation](#model-evaluation)
- [Results](#results)
- [Screenshots](#screenshots)
- [How to Use](#how-to-use)
- [Contributing](#contributing)
- [License](#license)

## Getting Started

### Prerequisites

- Python 3.x
- Jupyter Notebook (for running the project interactively)

### Installation

1. Clone the repository:

```bash
git clone https://github.com/Rahul-Sahani04/MovieReviewSentimentAnalysis.git
```
2. Install the required dependencies:

```bash
pip install -r requirements.txt
```
3. Open the Jupyter Notebook:

```bash
jupyter notebook
```

4. Open and Run all the cell in the MovieReview.py

## Project Structure

The project is organized into several components to facilitate a structured and modular approach.

- **`MovieReview.ipynb`**: The Main Jupyter Notebook.
- **`web_scraping.ipynb`**: Jupyter Notebook dedicated to web scraping IMDb movie reviews.
- **`data_preprocessing.ipynb`**: Jupyter Notebook focused on cleaning and preparing the scraped data through text cleaning, tokenization, and lemmatization.
- **`sentiment_analysis.ipynb`**: Jupyter Notebook handling sentiment analysis using the VADER sentiment analysis tool, with a focus on classifying reviews as positive or negative.
- **`results/`**: Directory containing scraped, processed data for reference and visualizations or images generated during the analysis.
- **`README.md`**: Project documentation providing an overview and guidance.

### Web Scraping

The `web_scraping.ipynb` notebook utilizes the BeautifulSoup library to scrape IMDb movie reviews. Reviews are extracted from the IMDb website and stored for subsequent analysis.

### Data Preprocessing

The `data_preprocessing.ipynb` notebook concentrates on the cleaning and preparation of the scraped data. Techniques such as text cleaning, tokenization, and lemmatization are applied to enhance the quality of the textual data.

### Sentiment Analysis

In the `sentiment_analysis.ipynb` notebook, sentiment analysis is conducted using the VADER sentiment analysis tool. The compound score is employed to classify each review as positive or negative, providing a comprehensive understanding of the sentiments expressed.

### Model Evaluation

The performance of the sentiment analysis model is thoroughly evaluated in terms of accuracy and classification reports. These results offer insights into the model's effectiveness in predicting sentiment.

### Results

Visualizations and results of the sentiment analysis are stored in the `results/` directory. These include the distribution of predicted sentiments and other relevant visualizations that aid in interpreting the sentiment analysis outcomes.

### Screenshots

Step 1:
![Screenshot 1](https://github.com/Rahul-Sahani04/MovieReviewSentimentAnalysis/blob/main/screenshots/Streamlit1.png?raw=true)

Step 2:
![Screenshot 2](https://github.com/Rahul-Sahani04/MovieReviewSentimentAnalysis/blob/main/screenshots/Streamlit2.png?raw=true)

### How to Use

To utilize the project, follow the steps outlined in the [Getting Started](#getting-started) section to set up the environment locally. Execute all the cell Jupyter Notebooks named `MovieReview.py` to perform sentiment analysis on IMDb movie reviews.

### Contributing

If you wish to contribute to the project, please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) guidelines for details on how to get involved and contribute effectively.

### License

This project is licensed under the MIT License, ensuring an open and collaborative development environment.