KanCMD: Kannada CodeMixed Dataset for Sentiment Analysis and Offensive Language Detection

Adeep Hande, Ruba Priyadharshini, Bharathi Raja Chakravarthi


Abstract
We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.
Anthology ID:
2020.peoples-1.6
Volume:
Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venues:
COLING | PEOPLES
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
54–63
Language:
URL:
https://www.aclweb.org/anthology/2020.peoples-1.6
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.peoples-1.6.pdf