The rapid advancement of social media enables us to analyze user opinions. In
recent times, sentiment analysis has shown a prominent research gap in
understanding human sentiment based on the content shared on social media.
Although sentiment analysis for commonly spoken languages has advanced
significantly, low-resource languages like Arabic continue to get little
research due to resource limitations. In this study, we explore sentiment
analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset.
Moreover, We investigated four pretrained language models and proposed two
ensemble language models. Our findings include monolingual models exhibiting
superior performance and ensemble models outperforming the baseline while the
majority voting ensemble outperforms the English language.

社交媒体的快速发展使得我们能够分析用户意见。尽管常用语言的情感分析已经取得了显著的进展，但由于资源限制，低资源语言像阿拉伯语等仍然很少有研究。本研究探讨了 SemEval-17 和阿拉伯语情感推文数据集上的推文文本的情感分析，并研究了四种预训练语言模型以及提出了两种集成语言模型。我们的发现包括单语言模型表现出更好的性能，集成模型优于基线，而多数投票集成模型胜过英语。