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

Saheed Salahudeen Abdullahi, Falalu Ibrahim Lawan, Ahmad Mustapha Wali, Amina Abubakar Imam, Aliyu Rabiu Shuaibu, Yusuf Aliyu, Nur Bala Rabiu, Musa Bello, Shamsuddeen Umar Adamu, Saminu Mohammad Aliyu, Murja Sani Gadanya, Sanah Abdullahi Muaz, Mahmoud Said Ahmad, Abdulkadir Abdullahi, Abdulmalik Yusuf Jamoh

Keywords: SemEval, sentiment analysis, low resource, monolingual, multilingual, natural language processing

Abstract: 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.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2304.13634/code)