TriLex: A Framework for Multilingual Sentiment Analysis in Low-Resource South African Languages

TL;DR

TriLex is a novel three-stage framework that systematically expands sentiment lexicons for low-resource South African languages, significantly improving the performance of multilingual sentiment analysis models like AfroXLMR and AfriBERTa.

Contribution

The paper introduces TriLex, a scalable framework combining corpus extraction, cross-lingual mapping, and retrieval augmented generation for sentiment lexicon expansion in low-resource languages.

Findings

AfroXLMR achieves F1-scores above 80% for isiXhosa and isiZulu.

AfriBERTa attains around 64% F1-score despite limited pre-training.

Both models outperform traditional machine learning baselines.

Abstract

Low-resource African languages remain underrepresented in sentiment analysis, limiting both lexical coverage and the performance of multilingual Natural Language Processing (NLP) systems. This study proposes TriLex, a three-stage retrieval augmented framework that unifies corpus-based extraction, cross lingual mapping, and retrieval augmented generation (RAG) driven lexical refinement to systematically expand sentiment lexicons for low-resource languages. Using the enriched lexicon, the performance of two prominent African pretrained language models (AfroXLMR and AfriBERTa) is evaluated across multiple case studies. Results demonstrate that AfroXLMR delivers superior performance, achieving F1-scores above 80% for isiXhosa and isiZulu and exhibiting strong cross-lingual stability. Although AfriBERTa lacks pre-training on these target languages, it still achieves reliable F1-scores around 64%, validating its utility in computationally constrained settings. Both models outperform traditional machine learning baselines, and ensemble analyses further enhance precision and robustness. The findings establish TriLex as a scalable and effective framework for multilingual sentiment lexicon expansion and sentiment modeling in low-resource South African languages.