HauBERT: A Transformer Model for Aspect-Based Sentiment Analysis of Hausa-Language Movie Reviews

In this study, we present a groundbreaking approach to aspect-based sentiment analysis (ABSA) using transformer-based models. ABSA is essential for understanding the intricate nuances of sentiment expressed in text, particularly across diverse linguistic and cultural contexts. Focusing on movie reviews in Hausa, a language under-represented in sentiment analysis research, we propose HauBERT, a bidirectional transformer-based approach tailored for aspect and polarity classification, by fine-tuning a pre-trained mBERT model. Our work addresses the scarcity of resources for sentiment analysis in under-represented languages by creating a comprehensive Hausa ABSA dataset. Leveraging this dataset, we preprocess the text using state-of-the-art techniques for feature extraction, enhancing the model’s ability to capture nuanced aspects of sentiment. Furthermore, we manually annotate aspect-level feature ontology words and sentiment polarity assignments within the reviewed text, enriching the dataset with valuable semantic information. Our proposed transformer-based model utilizes self-attention mechanisms to capture long-range dependencies and contextual information, enabling it to effectively analyze sentiment in Hausa movie reviews. The proposed model achieves significant accuracy in aspect term extraction and sentiment polarity classification, with scores of 99% and 92% respectively, outperforming traditional machine models. This demonstrates the transformer’s ability to capture complex linguistic patterns and nuances of sentiment. Our study advances ABSA research and contributes to a more inclusive sentiment analysis landscape by providing resources and models tailored for under-represented languages.