@inproceedings{p-etal-2025-codecrackers,
title = "codecrackers@{D}ravidian{L}ang{T}ech 2025: Sentiment Classification in {T}amil and {T}ulu Code-Mixed Social Media Text Using Machine Learning",
author = "P, Lalith Kishore V and
Manikandan, Dr G and
A, Mohan Raj M and
A, Keerthi Vasan and
M, Aravindh",
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-1.69/",
doi = "10.18653/v1/2025.dravidianlangtech-1.69",
pages = "387--391",
ISBN = "979-8-89176-228-2",
abstract = "Sentiment analysis of code-mixed Dravidian languages has become a major area of concern with increasing volumes of multilingual and code-mixed information across social media. This paper presents the ``Seventh Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu'', which was held as part of DravidianLangTech (NAACL-2025). However, sentiment analysis for code-mixed Dravidian languages has received little attention due to challenges such as class imbalance, small sample size, and the informal nature of the code-mixed text. This study applied an SVM-based approach for the sentiment classification of both Tamil and Tulu languages. The SVM model achieved competitive macro-average F1 scores of 0.54 for Tulu and 0.438 for Tamil, demonstrating that traditional machine learning methods can effectively tackle sentiment categorization in code-mixed languages under low-resource settings."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="p-etal-2025-codecrackers">
<titleInfo>
<title>codecrackers@DravidianLangTech 2025: Sentiment Classification in Tamil and Tulu Code-Mixed Social Media Text Using Machine Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lalith</namePart>
<namePart type="given">Kishore</namePart>
<namePart type="given">V</namePart>
<namePart type="family">P</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dr</namePart>
<namePart type="given">G</namePart>
<namePart type="family">Manikandan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohan</namePart>
<namePart type="given">Raj</namePart>
<namePart type="given">M</namePart>
<namePart type="family">A</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Keerthi</namePart>
<namePart type="given">Vasan</namePart>
<namePart type="family">A</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aravindh</namePart>
<namePart type="family">M</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>Sentiment analysis of code-mixed Dravidian languages has become a major area of concern with increasing volumes of multilingual and code-mixed information across social media. This paper presents the “Seventh Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu”, which was held as part of DravidianLangTech (NAACL-2025). However, sentiment analysis for code-mixed Dravidian languages has received little attention due to challenges such as class imbalance, small sample size, and the informal nature of the code-mixed text. This study applied an SVM-based approach for the sentiment classification of both Tamil and Tulu languages. The SVM model achieved competitive macro-average F1 scores of 0.54 for Tulu and 0.438 for Tamil, demonstrating that traditional machine learning methods can effectively tackle sentiment categorization in code-mixed languages under low-resource settings.</abstract>
<identifier type="citekey">p-etal-2025-codecrackers</identifier>
<identifier type="doi">10.18653/v1/2025.dravidianlangtech-1.69</identifier>
<location>
<url>https://aclanthology.org/2025.dravidianlangtech-1.69/</url>
</location>
<part>
<date>2025-05</date>
<extent unit="page">
<start>387</start>
<end>391</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T codecrackers@DravidianLangTech 2025: Sentiment Classification in Tamil and Tulu Code-Mixed Social Media Text Using Machine Learning
%A P, Lalith Kishore V.
%A Manikandan, Dr G.
%A A, Mohan Raj M.
%A A, Keerthi Vasan
%A M, Aravindh
%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 p-etal-2025-codecrackers
%X Sentiment analysis of code-mixed Dravidian languages has become a major area of concern with increasing volumes of multilingual and code-mixed information across social media. This paper presents the “Seventh Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu”, which was held as part of DravidianLangTech (NAACL-2025). However, sentiment analysis for code-mixed Dravidian languages has received little attention due to challenges such as class imbalance, small sample size, and the informal nature of the code-mixed text. This study applied an SVM-based approach for the sentiment classification of both Tamil and Tulu languages. The SVM model achieved competitive macro-average F1 scores of 0.54 for Tulu and 0.438 for Tamil, demonstrating that traditional machine learning methods can effectively tackle sentiment categorization in code-mixed languages under low-resource settings.
%R 10.18653/v1/2025.dravidianlangtech-1.69
%U https://aclanthology.org/2025.dravidianlangtech-1.69/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.69
%P 387-391
Markdown (Informal)
[codecrackers@DravidianLangTech 2025: Sentiment Classification in Tamil and Tulu Code-Mixed Social Media Text Using Machine Learning](https://aclanthology.org/2025.dravidianlangtech-1.69/) (P et al., DravidianLangTech 2025)
ACL
- Lalith Kishore V P, Dr G Manikandan, Mohan Raj M A, Keerthi Vasan A, and Aravindh M. 2025. codecrackers@DravidianLangTech 2025: Sentiment Classification in Tamil and Tulu Code-Mixed Social Media Text Using Machine Learning. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 387–391, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.