@inproceedings{gokhale-etal-2022-optimize,
title = "{O}ptimize{\_}{P}rime@{D}ravidian{L}ang{T}ech-{ACL}2022: Emotion Analysis in {T}amil",
author = "Gokhale, Omkar and
Patankar, Shantanu and
Litake, Onkar and
Mandke, Aditya and
Kadam, Dipali",
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-1.35",
doi = "10.18653/v1/2022.dravidianlangtech-1.35",
pages = "229--234",
abstract = "This paper aims to perform an emotion analysis of social media comments in Tamil. Emotion analysis is the process of identifying the emotional context of the text. In this paper, we present the findings obtained by Team Optimize{\_}Prime in the ACL 2022 shared task {``}Emotion Analysis in Tamil.{''} The task aimed to classify social media comments into categories of emotion like Joy, Anger, Trust, Disgust, etc. The task was further divided into two subtasks, one with 11 broad categories of emotions and the other with 31 specific categories of emotion. We implemented three different approaches to tackle this problem: transformer-based models, Recurrent Neural Networks (RNNs), and Ensemble models. XLM-RoBERTa performed the best on the first task with a macro-averaged f1 score of 0.27, while MuRIL provided the best results on the second task with a macro-averaged f1 score of 0.13.",
}
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<abstract>This paper aims to perform an emotion analysis of social media comments in Tamil. Emotion analysis is the process of identifying the emotional context of the text. In this paper, we present the findings obtained by Team Optimize_Prime in the ACL 2022 shared task “Emotion Analysis in Tamil.” The task aimed to classify social media comments into categories of emotion like Joy, Anger, Trust, Disgust, etc. The task was further divided into two subtasks, one with 11 broad categories of emotions and the other with 31 specific categories of emotion. We implemented three different approaches to tackle this problem: transformer-based models, Recurrent Neural Networks (RNNs), and Ensemble models. XLM-RoBERTa performed the best on the first task with a macro-averaged f1 score of 0.27, while MuRIL provided the best results on the second task with a macro-averaged f1 score of 0.13.</abstract>
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%0 Conference Proceedings
%T Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil
%A Gokhale, Omkar
%A Patankar, Shantanu
%A Litake, Onkar
%A Mandke, Aditya
%A Kadam, Dipali
%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 gokhale-etal-2022-optimize
%X This paper aims to perform an emotion analysis of social media comments in Tamil. Emotion analysis is the process of identifying the emotional context of the text. In this paper, we present the findings obtained by Team Optimize_Prime in the ACL 2022 shared task “Emotion Analysis in Tamil.” The task aimed to classify social media comments into categories of emotion like Joy, Anger, Trust, Disgust, etc. The task was further divided into two subtasks, one with 11 broad categories of emotions and the other with 31 specific categories of emotion. We implemented three different approaches to tackle this problem: transformer-based models, Recurrent Neural Networks (RNNs), and Ensemble models. XLM-RoBERTa performed the best on the first task with a macro-averaged f1 score of 0.27, while MuRIL provided the best results on the second task with a macro-averaged f1 score of 0.13.
%R 10.18653/v1/2022.dravidianlangtech-1.35
%U https://aclanthology.org/2022.dravidianlangtech-1.35
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.35
%P 229-234
Markdown (Informal)
[Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil](https://aclanthology.org/2022.dravidianlangtech-1.35) (Gokhale et al., DravidianLangTech 2022)
ACL