Reddit Sentiment Analysis

Table of Contents

• Overview
• Features
• Technologies Used
• Installation
• Usage
• Deployment
• Contributing
• License
• Acknowledgements

Overview

The Reddit Sentiment Analysis application analyzes comments from Reddit posts to determine the sentiment expressed by users. Leveraging Natural Language Processing (NLP) techniques, this project provides insights into community opinions across various topics, enhancing understanding of public sentiment.

Features

• Sentiment Classification: Classifies Reddit comments into positive, negative, or neutral sentiments.
• Data Visualization: Displays sentiment trends and distributions using interactive graphs.
• User-Friendly Interface: Designed for intuitive user interactions and ease of use.

Technologies Used

• Python: Programming language for the backend.
• Flask: Web framework for building the web application.
• Scikit-learn: Library for machine learning and model building.
• NLTK: Toolkit for natural language processing.
• Pandas: Data manipulation and analysis library.
• Matplotlib/Seaborn: Libraries for data visualization.
• Docker: Containerization technology for streamlined deployment.

Installation

Follow the steps below to set up the project locally:

1. Clone the repository:

   git clone https://github.com/mdayanabbas/Reddit_Sentiment_analysis.git
   cd Reddit_Sentiment_analysis

Create a virtual environment (recommended):

bash Copy code python -m venv venv source venv/bin/activate # Use venv\Scripts\activate on Windows Install the required packages:

bash Copy code pip install -r requirements.txt Set up environment variables: Ensure you have the following environment variables set in your .env file or system:

CLIENT_ID: Your Reddit API client ID. CLIENT_SECRET: Your Reddit API client secret. USER_AGENT: Your Reddit API user agent. Run the application:

bash Copy code python app.py

Usage

Open your web browser and navigate to http://localhost:5000. Enter the Reddit post URL or subreddit you wish to analyze. View the resulting sentiment analysis and visualizations.

Deployment

The application is deployed on Render. You can access it here.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

Fork the repository. Create a new branch for your feature or bug fix. Make your changes and commit them. Push your branch to your forked repository. Submit a pull request for review. License This project is licensed under the MIT License. See the LICENSE file for more details.