TriLex：一种用于低资源南非语言多语言情感分析的框架
TriLex: A Framework for Multilingual Sentiment Analysis in Low-Resource South African Languages
AI总结：
TriLex框架通过三阶段检索增强方法，系统扩展低资源南非语言的情感词典，提升多语言情感分析性能。
摘要：
低资源非洲语言在情感分析中仍处于劣势，限制了词汇覆盖范围和多语言自然语言处理（NLP）系统的性能。本研究提出TriLex，一种三阶段检索增强框架，通过基于语料库的提取、跨语言映射和检索增强生成（RAG）驱动的词汇细化，系统地扩展低资源语言的情感词典。利用丰富后的词典，对两种著名的非洲预训练语言模型（AfroXLMR和AfriBERTa）在多个案例研究中进行评估。结果表明，AfroXLMR在isiXhosa和isiZulu上实现了超过80%的F1分数，并表现出良好的跨语言稳定性。尽管AfriBERTa没有在这些目标语言上进行预训练，但其仍实现了约64%的可靠F1分数，验证了其在计算受限环境中的实用性。两种模型均优于传统机器学习基线，且组合分析进一步增强了精度和鲁棒性。研究结果确立了TriLex作为多语言情感词典扩展和低资源南非语言情感建模的可扩展且有效的框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
