We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. The task featured three subtasks; subtask A is monolingual sentiment classification with 12 tracks which are all monolingual languages, subtask B is multilingual sentiment classification using the tracks in subtask A and subtask C is a zero-shot sentiment classification. We present the results and findings of subtask A, subtask B and subtask C. We also release the code on github. Our goal 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 African languages. The datasets for these subtasks consists of a gold standard multi-class labeled Twitter datasets from these languages. Our results demonstrate that Afro-xlmr-large model performed better compared to the other models in most of the languages datasets. Similarly, Nigerian languages: Hausa, Igbo, and Yoruba achieved better performance compared to other languages and this can be attributed to the higher volume of data present in the languages.
@article{arxiv.2304.13634,
title = {HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis},
author = {Saheed Abdullahi Salahudeen and Falalu Ibrahim Lawan and Ahmad Mustapha Wali and Amina Abubakar Imam and Aliyu Rabiu Shuaibu and Aliyu Yusuf and Nur Bala Rabiu and Musa Bello and Shamsuddeen Umaru Adamu and Saminu Mohammad Aliyu and Murja Sani Gadanya and Sanah Abdullahi Muaz and Mahmoud Said Ahmad and Abdulkadir Abdullahi and Abdulmalik Yusuf Jamoh},
journal= {arXiv preprint arXiv:2304.13634},
year = {2023}
}