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We present the findings of SemEval-2023 Task 12, a shared task on sentimentanalysis for low-resource African languages using Twitter dataset. The taskfeatured three subtasks; subtask A is monolingual sentiment classification with12 tracks which are all monolingual languages, subtask B is multilingualsentiment classification using the tracks in subtask A and subtask C is azero-shot sentiment classification. We present the results and findings ofsubtask A, subtask B and subtask C. We also release the code on github. Ourgoal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large,AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert),Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 Africanlanguages. The datasets for these subtasks consists of a gold standardmulti-class labeled Twitter datasets from these languages. Our resultsdemonstrate that Afro-xlmr-large model performed better compared to the othermodels in most of the languages datasets. Similarly, Nigerian languages: Hausa,Igbo, and Yoruba achieved better performance compared to other languages andthis can be attributed to the higher volume of data present in the languages.

HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis