Bhattacharya_Lab at SemEval-2023 Task 12: A Transformer-based Language Model for Sentiment Classification for Low Resource African Languages: Nigerian Pidgin and Yoruba

Sentiment Analysis is an aspect of natural language processing (NLP) that has been a topic of research. While most studies focus on high-resource languages with an extensive amount of available data, the study on low-resource languages with insufficient data needs attention. To address this issue, we propose a transformer-based method for sentiment analysis for low-resources African languages, Nigerian Pidgin and Yoruba. To evaluate the effectiveness of our multilingual language models for monolingual sentiment classification, we participated in the AfriSenti SemEval shared task 2023 competition. On the official evaluation set, our group (named as Bhattacharya_Lab) ranked 1 out of 33 participating groups in the Monolingual Sentiment Classification task (i.e., Task A) for Nigerian Pidgin (i.e., Track 4), and in the Top 5 among 33 participating groups in the Monolingual Sentiment Classification task for Yoruba (i.e., Track 2) respectively, demonstrating the potential for our transformer-based language models to improve sentiment analysis in low-resource languages. Overall, our study highlights the importance of exploring the potential of NLP in low-resource languages and the impact of transformer-based multilingual language models in sentiment analysis for the low-resource African languages, Nigerian Pidgin and Yoruba.