Identifying Emotions in Code Mixed Hindi—English Tweets

Sonu, Sanket, Haque, Rejwanul, Hasanuzzaman, Mohammed, Stynes, Paul and Pathak, Pramod (2022) Identifying Emotions in Code Mixed Hindi—English Tweets. In: Proceedings of the WILDRE-6 Workshop @LREC2020. European Language Resources Association (ELRA), pp. 35-41. ISBN 979-10-95546-87-0

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Abstract

Emotion detection (ED) in tweets is a text classification problem that is of interest to Natural Language Processing (NLP) researchers. Code-mixing (CM) is a process of mixing linguistic units such as words of two different languages. The CM languages are characteristically different from the languages whose linguistic units are used for mixing. Whilst NLP has been shown to be successful for low-resource languages, it becomes challenging to perform NLP tasks on CM languages. As for ED, it has been rarely investigated on CM languages such as Hindi—English due to the lack of training data that is required for today’s data-driven classification algorithms. This research proposes a gold standard dataset for detecting emotions in CM Hindi–English tweets. This paper also presents our results about the investigation of the usefulness of our gold-standard dataset while testing a number of state-of-the-art classification algorithms. We found that the ED classifier built using SVM provided us the highest accuracy (75.17%) on the hold-out test set. This research would benefit the NLP community in detecting emotions from social media platforms in multilingual societies.

Item Type:	Book Section
Uncontrolled Keywords:	Emotion Detection; BERT; Code-mixing
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science B Philosophy. Psychology. Religion > Psychology > Emotions P Language and Literature > P Philology. Linguistics > Language Services
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Clara Chan
Date Deposited:	27 Sep 2022 12:08
Last Modified:	27 Sep 2022 12:23
URI:	https://norma.ncirl.ie/id/eprint/5793

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