  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.
