Abstract
We describe our work on sentiment analysis for Hausa, where we investigated monolingual and cross-lingual approaches to classify student comments in course evaluations. Furthermore, we propose a novel stemming algorithm to improve accuracy. For studies in this area, we collected a corpus of more than 40,000 comments—the Hausa-English Sentiment Analysis Corpus For Educational Environments (HESAC). Our results demonstrate that the monolingual approaches for Hausa sentiment analysis slightly outperform the cross-lingual systems. Using our stemming algorithm in the pre-processing even improved the best model resulting in 97.4% accuracy on HESAC.- Anthology ID:
- 2022.sigul-1.13
- Volume:
- Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Maite Melero, Sakriani Sakti, Claudia Soria
- Venue:
- SIGUL
- SIG:
- SIGUL
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 98–105
- Language:
- URL:
- https://aclanthology.org/2022.sigul-1.13
- DOI:
- Cite (ACL):
- Ochilbek Rakhmanov and Tim Schlippe. 2022. Sentiment Analysis for Hausa: Classifying Students’ Comments. In Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages, pages 98–105, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Sentiment Analysis for Hausa: Classifying Students’ Comments (Rakhmanov & Schlippe, SIGUL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2022.sigul-1.13.pdf