Kasaba6330/yoruba-sentiment-checker

A model to check the sentiment of a Yoruba text. Users can interact via the streamlit webapp. # 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. yoruba-sentiment-checker.st… **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** This demo assumes you are already familiar with python! Using the wildcard ('*') imports the following, to import one or more specific function, you can just call from any of the below mentioned: - vectorizer (This is the actual vectorizer) - app_pred (This is the function that does the sentiment analysis. It takes a string!) - sentiment_model (This is the trained model) - preprocess_text (This is a preprocessing function for Yorùbá texts.) - yoruba_stopwords (This is an iterable of stop words identified in the Yorùbá language) - positive_words (This is an iterable of words with positive connotations in the Yorùbá language) - negative_words (This is an iterable of words with negative connotations in the Yorùbá language) - neutral_words (This is an iterable of words with neutral connotations in the Yorùbá language) `pip install yorsent` `from yorsent import *` `text = 'Òru là ń ṣ'èkà, ẹni tí ó bá ṣe é lọ́sàn-án ò ní fi ara ire lọ.'` `sent = app_pred(text)` `print(sent)` …