SSNCSE_NLP@TamilNLP-ACL2022: Transformer based approach for Emotion analysis in Tamil language

Bharathi B, Josephine Varsha


Abstract
Emotion analysis is the process of identifying and analyzing the underlying emotions expressed in textual data. Identifying emotions from a textual conversation is a challenging task due to the absence of gestures, vocal intonation, and facial expressions. Once the chatbots and messengers detect and report the emotions of the user, a comfortable conversation can be carried out with no misunderstandings. Our task is to categorize text into a predefined notion of emotion. In this thesis, it is required to classify text into several emotional labels depending on the task. We have adopted the transformer model approach to identify the emotions present in the text sequence. Our task is to identify whether a given comment contains emotion, and the emotion it stands for. The datasets were provided to us by the LT-EDI organizers (CITATION) for two tasks, in the Tamil language. We have evaluated the datasets using the pretrained transformer models and we have obtained the micro-averaged F1 scores as 0.19 and 0.12 for Task1 and Task 2 respectively.
Anthology ID:
2022.dravidianlangtech-1.20
Volume:
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
DravidianLangTech
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
125–131
Language:
URL:
https://aclanthology.org/2022.dravidianlangtech-1.20
DOI:
10.18653/v1/2022.dravidianlangtech-1.20
Bibkey:
Cite (ACL):
Bharathi B and Josephine Varsha. 2022. SSNCSE_NLP@TamilNLP-ACL2022: Transformer based approach for Emotion analysis in Tamil language. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 125–131, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
SSNCSE_NLP@TamilNLP-ACL2022: Transformer based approach for Emotion analysis in Tamil language (B & Varsha, DravidianLangTech 2022)
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