README.md

metadata

title: Alkaike
emoji: 📚
colorFrom: blue
colorTo: pink
sdk: streamlit
sdk_version: 1.43.2
app_file: app.py
pinned: false
license: mit
short_description: news sentiment analysis

News Sentiment Analysis

This project, Alkaike, is a Streamlit-based application designed to perform sentiment analysis on news articles. It leverages natural language processing techniques to determine the sentiment (positive, negative, or neutral) of news content. Additionally, it includes features for Hindi text-to-speech (TTS), article comparison, and summarization.

Features

• Sentiment Analysis: Analyze the sentiment of news articles in real-time.
• Hindi Text-to-Speech (TTS): Convert Hindi text into speech for better accessibility.
• Article Comparison: Compare multiple news articles to identify similarities or differences.
• Article Summarization: Generate concise summaries of lengthy news articles.
• User-Friendly Interface: Built with Streamlit for an interactive and intuitive user experience.
• Customizable: Easily extendable to include additional features or datasets.

Installation

1. Clone the repository:

   git clone https://github.com/gouravchahar13/alkaike.git
   

2. Navigate to the project directory:

   cd alkaike
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

Run the application locally:

streamlit run app.py

License

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

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to improve the project.

Acknowledgments

• Built using Streamlit.
• Inspired by advancements in natural language processing, sentiment analysis, and text-to-speech technologies.

Contact

For any inquiries or feedback, please reach out to the project maintainer.