Sentiment analysis of Mizo using lexical features in low resource based models

Sentiment analysis is a vital area of natural language processing (NLP) for interpreting emotions in user-generated content. Although significant progress has been made for widely spoken languages, low-resource languages such as Mizo remain underexplored. This study addresses this gap by developing the first comprehensive sentiment analysis framework for Mizo language. We created a meticulously annotated data set that captures positive, negative, and neutral sentiments. Using classical machine learning models enhanced with lexicon features and transfer learning with XLM-RoBERTa, we demonstrate the feasibility of sentiment analysis in low-resource settings. Our approach achieves an accuracy of 82% with Logistic Regression and 78% with XLM-RoBERTa, which establishes a benchmark for future research in Mizo sentiment analysis.