Resources and Experiments on Sentiment Classification for Georgian

Nicolas Stefanovitch, Jakub Piskorski, Sopho Kharazi


Abstract
This paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. The characteristics of these resources and the process of their creation are described in detail. The results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification are also reported. Both 3-label (positive, neutral, negative) and 4-label settings (same labels + mixed) are considered. The machine learning models explored include, i.a., logistic regression, SVMs, and transformed-based models. We also explore transfer learning- and translation-based (to a well-supported language) approaches. The obtained results for Georgian are on par with the state-of-the-art results in sentiment classification for well studied languages when using training data of comparable size.
Anthology ID:
2022.lrec-1.173
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1613–1621
Language:
URL:
https://aclanthology.org/2022.lrec-1.173
DOI:
Bibkey:
Cite (ACL):
Nicolas Stefanovitch, Jakub Piskorski, and Sopho Kharazi. 2022. Resources and Experiments on Sentiment Classification for Georgian. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1613–1621, Marseille, France. European Language Resources Association.
Cite (Informal):
Resources and Experiments on Sentiment Classification for Georgian (Stefanovitch et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.173.pdf