@InProceedings{rakhmanov-schlippe:2022:SIGUL,
  author    = {Rakhmanov, Ochilbek  and  Schlippe, Tim},
  title     = {Sentiment Analysis for Hausa: Classifying Students’ Comments},
  booktitle      = {Proceedings of the the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages},
  month          = {June},
  year           = {2022},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {98--105},
  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.},
  url       = {https://aclanthology.org/2022.sigul-1.13}
}