@inproceedings{shanmugavadivel-etal-2024-code,
title = "{C}ode{\_}{M}akers@{D}ravidian{L}ang{T}ech-{EACL} 2024 : Sentiment Analysis in Code-Mixed {T}amil using Machine Learning Techniques",
author = "Shanmugavadivel, Kogilavani and
J S, Sowbharanika and
K, Navbila and
Subramanian, Malliga",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.21",
pages = "129--133",
abstract = "The rising importance of sentiment analysis online community research is addressed in our project, which focuses on the surge of code-mixed writing in multilingual social media. Targeting sentiments in texts combining Tamil and English, our supervised learning approach, particularly the Decision Tree algorithm, proves essential for effective sentiment classification. Notably, Decision Tree(accuracy: 0.99, average F1 score: 0.39), Random Forest exhibit high accuracy (accuracy: 0.99, macro average F1 score : 0.35), SVM (accuracy: 0.78, macro average F1 score : 0.68), Logistic Regression (accuracy: 0.75, macro average F1 score: 0.62), KNN (accuracy: 0.73, macro average F1 score : 0.26) also demonstrate commendable results. These findings showcase the project{'}s efficacy, offering promise for linguistic research and technological advancements. Securing the 8th rank emphasizes its recognition in the field.",
}
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%0 Conference Proceedings
%T Code_Makers@DravidianLangTech-EACL 2024 : Sentiment Analysis in Code-Mixed Tamil using Machine Learning Techniques
%A Shanmugavadivel, Kogilavani
%A J S, Sowbharanika
%A K, Navbila
%A Subramanian, Malliga
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F shanmugavadivel-etal-2024-code
%X The rising importance of sentiment analysis online community research is addressed in our project, which focuses on the surge of code-mixed writing in multilingual social media. Targeting sentiments in texts combining Tamil and English, our supervised learning approach, particularly the Decision Tree algorithm, proves essential for effective sentiment classification. Notably, Decision Tree(accuracy: 0.99, average F1 score: 0.39), Random Forest exhibit high accuracy (accuracy: 0.99, macro average F1 score : 0.35), SVM (accuracy: 0.78, macro average F1 score : 0.68), Logistic Regression (accuracy: 0.75, macro average F1 score: 0.62), KNN (accuracy: 0.73, macro average F1 score : 0.26) also demonstrate commendable results. These findings showcase the project’s efficacy, offering promise for linguistic research and technological advancements. Securing the 8th rank emphasizes its recognition in the field.
%U https://aclanthology.org/2024.dravidianlangtech-1.21
%P 129-133
Markdown (Informal)
[Code_Makers@DravidianLangTech-EACL 2024 : Sentiment Analysis in Code-Mixed Tamil using Machine Learning Techniques](https://aclanthology.org/2024.dravidianlangtech-1.21) (Shanmugavadivel et al., DravidianLangTech-WS 2024)
ACL