Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets
Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Journal of Information Management Data Insights |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096825000126 |
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| author | Yusuf Aliyu Aliza Sarlan Kamaluddeen Usman Danyaro Abdullahi Sani abd Rahman Aminu Aminu Muazu Mustapha Yusuf Abubakar |
| author_facet | Yusuf Aliyu Aliza Sarlan Kamaluddeen Usman Danyaro Abdullahi Sani abd Rahman Aminu Aminu Muazu Mustapha Yusuf Abubakar |
| author_sort | Yusuf Aliyu |
| collection | DOAJ |
| description | Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities. |
| format | Article |
| id | doaj-art-b8a2193d76e349f7b3babadbe9e7ce5d |
| institution | Kabale University |
| issn | 2667-0968 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-b8a2193d76e349f7b3babadbe9e7ce5d2025-08-20T03:45:47ZengElsevierInternational Journal of Information Management Data Insights2667-09682025-06-015110033010.1016/j.jjimei.2025.100330Deep learning techniques for sentiment analysis in code-switched Hausa-English tweetsYusuf Aliyu0Aliza Sarlan1Kamaluddeen Usman Danyaro2Abdullahi Sani abd Rahman3Aminu Aminu Muazu4Mustapha Yusuf Abubakar5Department of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610 Perak, Malaysia; Corresponding authors.Center for Foundation Studies, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, MalaysiaDepartment of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610 Perak, Malaysia; Centre for Cyber-Physical Systems (C2PS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia; Corresponding authors.Department of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610 Perak, MalaysiaDepartment of Computer and Information Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610 Perak, Malaysia; Computer Science Department, Faculty of Natural and Applied Science, Umaru Musa Yar'adua University, Katsina, NigeriaComputer Science Department, School of Technology, Kano State Polytechnic, Kano 700231, NigeriaSocial media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities.http://www.sciencedirect.com/science/article/pii/S2667096825000126Sentiment analysisLow-resourceCode-switchedHausa languageWord-embeddingTransformer |
| spellingShingle | Yusuf Aliyu Aliza Sarlan Kamaluddeen Usman Danyaro Abdullahi Sani abd Rahman Aminu Aminu Muazu Mustapha Yusuf Abubakar Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets International Journal of Information Management Data Insights Sentiment analysis Low-resource Code-switched Hausa language Word-embedding Transformer |
| title | Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets |
| title_full | Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets |
| title_fullStr | Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets |
| title_full_unstemmed | Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets |
| title_short | Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets |
| title_sort | deep learning techniques for sentiment analysis in code switched hausa english tweets |
| topic | Sentiment analysis Low-resource Code-switched Hausa language Word-embedding Transformer |
| url | http://www.sciencedirect.com/science/article/pii/S2667096825000126 |
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