MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis

Priya Rani, Theodorus Fransen, John P. McCrae, Gaurav Negi

Abstract
The present paper introduces new sentiment data, MaCMS, for Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a less-resourced minority language. This dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. Further, we also provide a linguistics analysis of the dataset to understand the structure of code-mixing and a statistical study to understand the language preferences of speakers with different polarities. With these analyses, we also train baseline models to evaluate the dataset’s quality.
Anthology ID:
2024.lrec-main.950
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
10880–10890
Language:
URL:
https://aclanthology.org/2024.lrec-main.950
DOI:
Bibkey:
Cite (ACL):
Priya Rani, Theodorus Fransen, John P. McCrae, and Gaurav Negi. 2024. MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10880–10890, Torino, Italia. ELRA and ICCL.
Cite (Informal):
MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis (Rani et al., LREC-COLING 2024)
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