COLING 2024main2 citations
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.
BibTeX
@inproceedings{rani-etal-2024-macms,
title = "{M}a{C}m{S}: {M}agahi Code-mixed Dataset for Sentiment Analysis",
author = "Rani, Priya and
Fransen, Theodorus and
McCrae, John P. and
Negi, Gaurav",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.950/",
pages = "10880--10890"
}