@inproceedings{s-etal-2022-varsini,
title = "{V}arsini{\_}and{\_}{K}irthanna@{D}ravidian{L}ang{T}ech-{ACL}2022-Emotional Analysis in {T}amil",
author = "S, Varsini and
Rajan, Kirthanna and
S, Angel and
Sivanaiah, Rajalakshmi and
Rajendram, Sakaya Milton and
T T, Mirnalinee",
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-1.26",
doi = "10.18653/v1/2022.dravidianlangtech-1.26",
pages = "165--169",
abstract = "In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.",
}
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<abstract>In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.</abstract>
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%0 Conference Proceedings
%T Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil
%A S, Varsini
%A Rajan, Kirthanna
%A S, Angel
%A Sivanaiah, Rajalakshmi
%A Rajendram, Sakaya Milton
%A T T, Mirnalinee
%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 s-etal-2022-varsini
%X In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.
%R 10.18653/v1/2022.dravidianlangtech-1.26
%U https://aclanthology.org/2022.dravidianlangtech-1.26
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.26
%P 165-169
Markdown (Informal)
[Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil](https://aclanthology.org/2022.dravidianlangtech-1.26) (S et al., DravidianLangTech 2022)
ACL