Unveiling Sentiments: A Deep Dive Into Sentiment Analysis for Low-Resource Languages-A Case Study on Hausa Texts

• Published in: IEEE access Vol. 12; pp. 98900 - 98916
• Main Authors: Shehu, Harisu Abdullahi, Usman Majikumna, Kaloma, Bashir Suleiman, Aminu, Luka, Stephen, Sharif, Md. Haidar, Ramadan, Rabie A., Kusetogullari, Huseyin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Africa; Artificial neural networks; Bag of words; Bag of words model; Case-studies; Convolutional neural network; Cultural differences; Data mining; Deep dives; Deep learning; Education; Hausa text; Hausa texts; Languages; lexicon dictionary; Linguistics; Low resource languages; Machine learning; Neural networks; Performance enhancement; Recurrent neural networks; Sentiment analysis; Socioeconomics; Urban areas; Words (language)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3427416

Opinion mining has witnessed significant advancements in well-resourced languages. However, for low-resource languages, this landscape remains relatively unexplored. This paper addresses this gap by conducting a comprehensive investigation into sentiment analysis in the context of Hausa, one of the most widely spoken languages within the Afro-Asiatic family. To resolve the problem, three different models based on Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Hierarchical Attention Network (HAN), all tailored to the unique linguistic characteristics of Hausa have been proposed. Additionally, we have developed the first dedicated lexicon dictionary for Hausa sentiment analysis and a customized stemming method to enhance the accuracy of the bag of words approach. Our results indicate that CNN and HAN achieved significantly higher performance compared to other models such as RNN. While the experimental results demonstrate the effectiveness of the developed deep learning models in contrast to the bag of words approach, the proposed stemming method was found to significantly improve the performance of the bag of words approach. The findings from this study not only enrich the sentiment analysis domain for Hausa but also provide a foundation for future research endeavors in similarly underrepresented languages.