Naomi-NLP/Yoruba_Sentiment_Analysis

# Yoruba Sentiment Analysis ## Live Demo Explore the web interface: Try the app live --- ## Overview This project develops a sentiment analysis system for **Yoruba text**, automatically classifying inputs into **Positive, Neutral, or Negative** categories. It contributes to **Natural Language Processing (NLP)** for African languages, particularly Yoruba. --- # 📂 Dataset This project is based on the **NaijaSenti** dataset, a large-scale collection of annotated Nigerian language tweets for sentiment analysis. For this project, the **Yoruba subset** of NaijaSenti was used, containing tweets labeled as **Positive, Neutral, or Negative**. - 📌 Dataset Source: NaijaSenti Yoruba Tweets ## Implementation Steps - **Data Preparation**: Cleaned and preprocessed Yoruba text noise removal, tone-mark normalization, tokenization. - **Modeling**: Trained a **Naive Bayes classifier** to categorize tweets by sentiment. - **Evaluation**: Used **accuracy**, **precision**, **recall**, **F1-score**, and a **confusion matrix** to evaluate classification performance. - **Deployment**: Created a live **Streamlit app** enabling users to input Yoruba text and instantly receive predicted sentiment. --- ## Significance - Enables **social media monitoring**, **customer feedback analysis**, and **opinion mining** in Yoruba. - Fills a crucial gap in computational tools for **Yoruba NLP**. - Demonstrates the applicability of lightweight models like **Naive Bayes** in low-resource settings. --- ## Dependencies - Python - pandas - numpy - scikit-learn - nltk - streamlit (for the interface) --- ## Usage 1. Clone the repository: ```bash git clone github.com cd yoruba-sentiment-analysis