@inproceedings{mia-etal-2025-kcrl,
title = "{KCRL}@{D}ravidian{L}ang{T}ech 2025: Multi-View Feature Fusion with {XLM}-{R} for {T}amil Political Sentiment Analysis",
author = "Mia, Md Ayon and
Haq, Fariha and
Shawon, Md. Tanvir Ahammed and
Md. Mursalin, Golam Sarwar and
Khan, Muhammad Ibrahim",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.108/",
doi = "10.18653/v1/2025.dravidianlangtech-1.108",
pages = "630--635",
ISBN = "979-8-89176-228-2",
abstract = "Political discourse on social media platforms significantly influences public opinion, necessitating accurate sentiment analysis for understanding societal perspectives. This paper presents a system developed for the shared task of Political Multiclass Sentiment Analysis in Tamil tweets. The task aims to classify tweets into seven distinct sentiment categories: Substantiated, Sarcastic, Opinionated, Positive, Negative, Neutral, and None of the above. We propose a Multi-View Feature Fusion (MVFF) architecture that leverages XLM-R with a CLS-Attention-Mean mechanism for sentiment classification. Our experimental results demonstrate the effectiveness of our approach, achieving a macro-average F1-score of 0.37 on the test set and securing the 2nd position in the shared task. Through comprehensive error analysis, we identify specific classification challenges and demonstrate how our model effectively navigates the linguistic complexities of Tamil political discourse while maintaining robust classification performance across multiple sentiment categories."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mia-etal-2025-kcrl">
<titleInfo>
<title>KCRL@DravidianLangTech 2025: Multi-View Feature Fusion with XLM-R for Tamil Political Sentiment Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Md</namePart>
<namePart type="given">Ayon</namePart>
<namePart type="family">Mia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fariha</namePart>
<namePart type="family">Haq</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Md.</namePart>
<namePart type="given">Tanvir</namePart>
<namePart type="given">Ahammed</namePart>
<namePart type="family">Shawon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Golam</namePart>
<namePart type="given">Sarwar</namePart>
<namePart type="family">Md. Mursalin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Muhammad</namePart>
<namePart type="given">Ibrahim</namePart>
<namePart type="family">Khan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Bharathi</namePart>
<namePart type="given">Raja</namePart>
<namePart type="family">Chakravarthi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruba</namePart>
<namePart type="family">Priyadharshini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anand</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Madasamy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sajeetha</namePart>
<namePart type="family">Thavareesan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Sherly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saranya</namePart>
<namePart type="family">Rajiakodi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Balasubramanian</namePart>
<namePart type="family">Palani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Malliga</namePart>
<namePart type="family">Subramanian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Subalalitha</namePart>
<namePart type="family">Cn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dhivya</namePart>
<namePart type="family">Chinnappa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-228-2</identifier>
</relatedItem>
<abstract>Political discourse on social media platforms significantly influences public opinion, necessitating accurate sentiment analysis for understanding societal perspectives. This paper presents a system developed for the shared task of Political Multiclass Sentiment Analysis in Tamil tweets. The task aims to classify tweets into seven distinct sentiment categories: Substantiated, Sarcastic, Opinionated, Positive, Negative, Neutral, and None of the above. We propose a Multi-View Feature Fusion (MVFF) architecture that leverages XLM-R with a CLS-Attention-Mean mechanism for sentiment classification. Our experimental results demonstrate the effectiveness of our approach, achieving a macro-average F1-score of 0.37 on the test set and securing the 2nd position in the shared task. Through comprehensive error analysis, we identify specific classification challenges and demonstrate how our model effectively navigates the linguistic complexities of Tamil political discourse while maintaining robust classification performance across multiple sentiment categories.</abstract>
<identifier type="citekey">mia-etal-2025-kcrl</identifier>
<identifier type="doi">10.18653/v1/2025.dravidianlangtech-1.108</identifier>
<location>
<url>https://aclanthology.org/2025.dravidianlangtech-1.108/</url>
</location>
<part>
<date>2025-05</date>
<extent unit="page">
<start>630</start>
<end>635</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T KCRL@DravidianLangTech 2025: Multi-View Feature Fusion with XLM-R for Tamil Political Sentiment Analysis
%A Mia, Md Ayon
%A Haq, Fariha
%A Shawon, Md. Tanvir Ahammed
%A Md. Mursalin, Golam Sarwar
%A Khan, Muhammad Ibrahim
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F mia-etal-2025-kcrl
%X Political discourse on social media platforms significantly influences public opinion, necessitating accurate sentiment analysis for understanding societal perspectives. This paper presents a system developed for the shared task of Political Multiclass Sentiment Analysis in Tamil tweets. The task aims to classify tweets into seven distinct sentiment categories: Substantiated, Sarcastic, Opinionated, Positive, Negative, Neutral, and None of the above. We propose a Multi-View Feature Fusion (MVFF) architecture that leverages XLM-R with a CLS-Attention-Mean mechanism for sentiment classification. Our experimental results demonstrate the effectiveness of our approach, achieving a macro-average F1-score of 0.37 on the test set and securing the 2nd position in the shared task. Through comprehensive error analysis, we identify specific classification challenges and demonstrate how our model effectively navigates the linguistic complexities of Tamil political discourse while maintaining robust classification performance across multiple sentiment categories.
%R 10.18653/v1/2025.dravidianlangtech-1.108
%U https://aclanthology.org/2025.dravidianlangtech-1.108/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.108
%P 630-635
Markdown (Informal)
[KCRL@DravidianLangTech 2025: Multi-View Feature Fusion with XLM-R for Tamil Political Sentiment Analysis](https://aclanthology.org/2025.dravidianlangtech-1.108/) (Mia et al., DravidianLangTech 2025)
ACL
- Md Ayon Mia, Fariha Haq, Md. Tanvir Ahammed Shawon, Golam Sarwar Md. Mursalin, and Muhammad Ibrahim Khan. 2025. KCRL@DravidianLangTech 2025: Multi-View Feature Fusion with XLM-R for Tamil Political Sentiment Analysis. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 630–635, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.