Sentiment Analysis for Hausa: Classifying Students’ Comments

Ochilbek Rakhmanov, Tim Schlippe


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2022.sigul-1.13.pdf