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In recent years, sentiment analysis has gained significant importance innatural language processing. However, most existing models and datasets forsentiment analysis are developed for high-resource languages, such as Englishand Chinese, leaving low-resource languages, particularly African languages,largely unexplored. The AfriSenti-SemEval 2023 Shared Task 12 aims to fill thisgap by evaluating sentiment analysis models on low-resource African languages.In this paper, we present our solution to the shared task, where we employeddifferent multilingual XLM-R models with classification head trained on variousdata, including those retrained in African dialects and fine-tuned on targetlanguages. Our team achieved the third-best results in Subtask B, Track 16:Multilingual, demonstrating the effectiveness of our approach. While our modelshowed relatively good results on multilingual data, it performed poorly insome languages. Our findings highlight the importance of developing morecomprehensive datasets and models for low-resource African languages to advancesentiment analysis research. We also provided the solution on the githubrepository.

DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning