sadegh15khedry/comments-sentiment-analysis

text classification on comments using an ANN model.

Comments Sentiment Analysis

Overview

Comments Sentiment Analysis is a project focused on analyzing the sentiment of user comments. It utilizes natural language processing (NLP) techniques to classify comments as positive, negative, or neutral. This project aims to provide insights into user opinions and feedback by automatically categorizing the sentiment of their comments.

Table of Contents

• Features
• Screenshots
• Technologies Used
• Installation
  • Prerequisites
  • Clone the Repository
  • Setup the Environment
  • Run the Application
• Usage
• Known Issues
• Future Plans
• Contributing
• License
• Contact

Features

• Sentiment Classification: Classify comments into positive, negative, or neutral sentiment categories.
• Data Preprocessing: Clean and preprocess text data to prepare it for sentiment analysis.
• Batch Processing: Process multiple comments at once to analyze overall sentiment trends.

Technologies Used

• Python
• Natural Language Processing (NLP) libraries such as NLTK or spaCy
• Machine Learning libraries such as scikit-learn

Installation

Prerequisites

• Python 3.x
• Virtual environment (optional but recommended)

Clone the Repository

1. Clone the repository:

   git clone https://github.com/sadegh15khedry/commentsSentimentAnalysis.git
   cd commentsSentimentAnalysis
   

Setup the Environment

2. Create and activate a virtual environment (optional):

   python -m venv venv
   source venv/bin/activate   # On Windows use `venv\Scripts\activate`
   

Run the Application

3. Run the sentiment analysis script:

   python sentiment_analysis.py
   

Usage

1. Input Comments: Provide comments either through a text file or directly in the script.
2. Run Analysis: Execute the script to analyze the sentiment of the provided comments.
3. View Results: Review the sentiment classification results and visualizations.

Known Issues

• This project is an initial implementation and may not handle all edge cases in text data.
• The accuracy of the sentiment analysis depends on the quality and size of the training data.

Future Plans

• Improve Sentiment Model: Enhance the sentiment analysis model by using more advanced machine learning algorithms and larger datasets.
• API Integration: Develop a RESTful API to allow other applications to use the sentiment analysis service.
• Real-time Analysis: Implement real-time sentiment analysis for streaming comments or live feedback.
• GUI Application: Create a graphical user interface to make the tool more user-friendly and accessible.

Contributing

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

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or inquiries, please feel free to open an issue on the GitHub repository.