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        <title>Multilingual Models for Sentiment and Abusive Language Detection for Dravidian Languages</title>
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            <namePart type="given">Bharathi</namePart>
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            <namePart type="family">Chakravarthi</namePart>
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    <abstract>This paper presents the TFIDF based LSTM and Hierarchical Attention Networks (HAN) for code-mixed abusive comment detection and sentiment analysis for Dravidian languages. The traditional TF-IDF-based techniques have out- performed the Hierarchical Attention models in both the sentiment analysis and abusive language detection tasks. The Tulu sentiment analysis system demonstrated better performance for the Positive and Neutral classes, whereas the Tamil sentiment analysis system exhibited lower performance overall. This highlights the need for more balanced datasets and additional research to enhance the accuracy of sentiment analysis in the Tamil language. In terms of abusive language detection, the TF-IDF-LSTM models generally outperformed the Hierarchical Attention models. However, the mixed models displayed better performance for specific classes such as “Homophobia” and “Xenophobia.” This implies that considering both code-mixed and original script data can offer a different perspective for research in social media analysis.</abstract>
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%0 Conference Proceedings
%T Multilingual Models for Sentiment and Abusive Language Detection for Dravidian Languages
%A M, Anand Kumar
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F m-2023-multilingual
%X This paper presents the TFIDF based LSTM and Hierarchical Attention Networks (HAN) for code-mixed abusive comment detection and sentiment analysis for Dravidian languages. The traditional TF-IDF-based techniques have out- performed the Hierarchical Attention models in both the sentiment analysis and abusive language detection tasks. The Tulu sentiment analysis system demonstrated better performance for the Positive and Neutral classes, whereas the Tamil sentiment analysis system exhibited lower performance overall. This highlights the need for more balanced datasets and additional research to enhance the accuracy of sentiment analysis in the Tamil language. In terms of abusive language detection, the TF-IDF-LSTM models generally outperformed the Hierarchical Attention models. However, the mixed models displayed better performance for specific classes such as “Homophobia” and “Xenophobia.” This implies that considering both code-mixed and original script data can offer a different perspective for research in social media analysis.
%U https://aclanthology.org/2023.ltedi-1.3/
%P 17-24

Markdown (Informal)

[Multilingual Models for Sentiment and Abusive Language Detection for Dravidian Languages](https://aclanthology.org/2023.ltedi-1.3/) (M, LTEDI 2023)

• Multilingual Models for Sentiment and Abusive Language Detection for Dravidian Languages (M, LTEDI 2023)

ACL

• Anand Kumar M. 2023. Multilingual Models for Sentiment and Abusive Language Detection for Dravidian Languages. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 17–24, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.