SharonMukiri/Automated-Sentimental-Analysis-System-for-Kenyan-Retail-Feedback

Multilingual sentiment analysis (English/Swahili/Sheng) for Kenyan retail feedback using Naive Bayes and SVM. # Multilingual Sentiment Analysis for Kenyan Retail Feedback A machine learning system for classifying customer sentiment in **English, Swahili, and Sheng**, built to reflect how Kenyan consumers actually write retail feedback online, rather than assuming a single-language input. ## Overview Most sentiment analysis tools are trained and tuned for standard English, which makes them unreliable for markets like Kenya where feedback is frequently written in code-switched language, blending English, Swahili, and Sheng within the same sentence or review. This project addresses that gap by building a sentiment classifier specifically trained and evaluated on multilingual, code-switched retail feedback. ## Key Features - **Multilingual support** — handles English, Swahili, and Sheng, including code-mixed text - **Dual-model approach** — implements and compares Naïve Bayes and Support Vector Machine (SVM) classifiers - **Retail-focused dataset** — trained on customer feedback representative of the Kenyan e-commerce/retail context - **86.3% accuracy** achieved on the evaluation set ## Motivation Kenyan businesses increasingly rely on online customer feedback to guide decisions, but off-the-shelf NLP tools often misclassify sentiment when text switches between languages mid-sentence, a common feature of everyday Kenyan communication. This project explores whether a model trained specifically on this kind of text can produce more reliable results than general-purpose sentiment tools. ## Tech Stack - Python - scikit-learn (Naïve Bayes, SVM) - NLP preprocessing for code-mixed/multilingual text ## Future Improvements - Expand training data across more retail categories - Explore transformer-based models (e.g. multilingual BERT) for comparison - Deploy as an API for real-time feedback classification --- *This project was developed as a final-year capstone at JKUAT (Jomo Kenyatta University of Agriculture and Technology).*