Sentiment analysis can aid in understanding people's opinions and emotions on social issues. In multilingual communities sentiment analysis systems can be used to quickly identify social challenges in social media posts, enabling government departments to detect and address these issues more precisely and effectively. Recently, large-language models (LLMs) have become available to the wide public and initial analyses have shown that they exhibit magnificent zero-shot sentiment analysis abilities in English. However, there is no work that has investigated to leverage LLMs for sentiment analysis on social media posts in South African languages and detect social challenges. Consequently, in this work, we analyse the zero-shot performance of the state-of-the-art LLMs GPT-3.5, GPT-4, LlaMa 2, PaLM 2, and Dolly 2 to investigate the sentiment polarities of the 10 most emerging topics in English, Sepedi and Setswana social media posts that fall within the jurisdictional areas of 10 South African government departments. Our results demonstrate that there are big differences between the various LLMs, topics, and languages. In addition, we show that a fusion of the outcomes of different LLMs provides large gains in sentiment classification performance with sentiment classification errors below 1%. Consequently, it is now feasible to provide systems that generate reliable information about sentiment analysis to detect social challenges and draw conclusions about possible needs for actions on specific topics and within different language groups.

翻译：情感分析有助于理解人们对社会问题的观点与情绪。在多语言社区中，情感分析系统可用于快速识别社交媒体帖子中的社会挑战，使政府部门能够更精准、有效地发现并应对这些问题。近年来，大语言模型（LLMs）已向公众广泛开放，初步分析表明其在英语情感分析中展现出卓越的零样本能力。然而，目前尚无研究探讨如何利用LLMs对南非语言的社交媒体帖子进行情感分析以检测社会挑战。为此，本研究分析了最先进的LLMs（包括GPT-3.5、GPT-4、LlaMa 2、PaLM 2和Dolly 2）的零样本性能，以探究10个南非政府部门管辖范围内、在英语、塞佩迪语和茨瓦纳语社交媒体帖子中最突出的10个话题的情感极性。研究结果表明，不同LLMs、话题及语言之间存在显著差异。此外，我们证明通过融合不同LLMs的输出结果，可大幅提升情感分类性能，其分类错误率低于1%。因此，现在已可构建能够生成可靠情感分析信息的系统，用于检测社会挑战，并就特定话题及不同语言群体中可能的行动需求得出有效结论。