@inproceedings{k-etal-2022-pandas,
title = "{PANDAS}@{T}amil{NLP}-{ACL}2022: Emotion Analysis in {T}amil Text using Language Agnostic Embeddings",
author = "K, Divyasri and
G L, Gayathri and
Swaminathan, Krithika and
Durairaj, Thenmozhi and
B, Bharathi and
B, Senthil Kumar",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.17",
doi = "10.18653/v1/2022.dravidianlangtech-1.17",
pages = "105--111",
abstract = "As the world around us continues to become increasingly digital, it has been acknowledged that there is a growing need for emotion analysis of social media content. The task of identifying the emotion in a given text has many practical applications ranging from screening public health to business and management. In this paper, we propose a language-agnostic model that focuses on emotion analysis in Tamil text. Our experiments yielded an F1-score of 0.010.",
}
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%0 Conference Proceedings
%T PANDAS@TamilNLP-ACL2022: Emotion Analysis in Tamil Text using Language Agnostic Embeddings
%A K, Divyasri
%A G L, Gayathri
%A Swaminathan, Krithika
%A Durairaj, Thenmozhi
%A B, Bharathi
%A B, Senthil Kumar
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F k-etal-2022-pandas
%X As the world around us continues to become increasingly digital, it has been acknowledged that there is a growing need for emotion analysis of social media content. The task of identifying the emotion in a given text has many practical applications ranging from screening public health to business and management. In this paper, we propose a language-agnostic model that focuses on emotion analysis in Tamil text. Our experiments yielded an F1-score of 0.010.
%R 10.18653/v1/2022.dravidianlangtech-1.17
%U https://aclanthology.org/2022.dravidianlangtech-1.17
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.17
%P 105-111
Markdown (Informal)
[PANDAS@TamilNLP-ACL2022: Emotion Analysis in Tamil Text using Language Agnostic Embeddings](https://aclanthology.org/2022.dravidianlangtech-1.17) (K et al., DravidianLangTech 2022)
ACL