yorsent 1.0

A Yoruba language model to predict sentiment of Yoruba texts

Yoruba-sentiment-checker

Yorùbá Sentiment Checker A hybrid (Rule-Based + Machine Learning) sentiment analysis tool for classifying Yorùbá text into Positive, Negative, or Neutral categories. This project addresses the challenge of building NLP tools for low-resource languages by combining the precision of a curated lexicon with the contextual understanding of a statistical model.

Features Hybrid Architecture: Integrates a comprehensive, hand-curated Yorùbá sentiment lexicon with a Logistic Regression classifier for robust sentiment prediction.

Web Application: A user-friendly Streamlit web app for real-time sentiment analysis of text or uploaded files. https://yoruba-sentiment-checker.streamlit.app/

Public Resources: Provides a valuable public sentiment lexicon and sentiment analysis language model for Yorùbá to support further NLP research.

Reproducible Research: Complete code and methodology are provided for full transparency and reproducibility.

Installation & Usage

Clone the repository:

bash

git clone https://github.com/Kasaba6330/yoruba-sentiment-checker.git

cd yoruba-sentiment-checker

Install dependencies:

bash

pip install -r requirements.txt

Run the web application:

bash

streamlit run app.py

Data

The model is trained on an extended dataset built upon the Yorùbá portion of the AfriSenti-SemEval dataset.

Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

License

This project is licensed under the Apache-2.0 License.