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.

翻译：低资源非洲语言在情感分析领域仍代表性不足，这既限制了词汇覆盖范围，也制约了多语言自然语言处理（NLP）系统的性能。本研究提出TriLex——一个三阶段检索增强框架，它整合了基于语料库的提取、跨语言映射以及检索增强生成（RAG）驱动的词汇精化，以系统性地扩展低资源语言的情感词典。利用该增强词典，本研究通过多个案例研究评估了两种主流的非洲预训练语言模型（AfroXLMR与AfriBERTa）的性能。结果表明，AfroXLMR表现更优，在科萨语和祖鲁语上取得了超过80%的F1分数，并展现出较强的跨语言稳定性。尽管AfriBERTa未针对这些目标语言进行预训练，其F1分数仍稳定在64%左右，验证了其在计算资源受限场景下的实用性。两种模型均优于传统的机器学习基线方法，集成分析进一步提升了精确度与鲁棒性。研究结果确立了TriLex作为一种可扩展且高效的框架，适用于低资源南非语言的多语言情感词典扩展与情感建模。