@inproceedings{shanmugavadivel-etal-2023-kec-ai,
title = "{KEC}{\_}{AI}{\_}{NLP}@{D}ravidian{L}ang{T}ech: Sentiment Analysis in Code Mixture Language",
author = "Shanmugavadivel, Kogilavani and
Subaramanian, Malliga and
S, VetriVendhan and
M, Pramoth Kumar and
S, Karthickeyan and
N, Kavin Vishnu",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.44",
pages = "300--305",
abstract = "Sentiment Analysis is a process that involves analyzing digital text to determine the emo- tional tone, such as positive, negative, neu- tral, or unknown. Sentiment Analysis of code- mixed languages presents challenges in natural language processing due to the complexity of code-mixed data, which combines vocabulary and grammar from multiple languages and cre- ates unique structures. The scarcity of anno- tated data and the unstructured nature of code- mixed data are major challenges. To address these challenges, we explored various tech- niques, including Machine Learning models such as Decision Trees, Random Forests, Lo- gistic Regression, and Gaussian Na ̈{\i}ve Bayes, Deep Learning model, such as Long Short- Term Memory (LSTM), and Transfer Learning model like BERT, were also utilized. In this work, we obtained the dataset from the Dravid- ianLangTech shared task by participating in a competition and accessing train, development and test data for Tamil Language. The results demonstrated promising performance in senti- ment analysis of code-mixed text. Among all the models, deep learning model LSTM pro- vides best accuracy of 0.61 for Tamil language.",
}
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<abstract>Sentiment Analysis is a process that involves analyzing digital text to determine the emo- tional tone, such as positive, negative, neu- tral, or unknown. Sentiment Analysis of code- mixed languages presents challenges in natural language processing due to the complexity of code-mixed data, which combines vocabulary and grammar from multiple languages and cre- ates unique structures. The scarcity of anno- tated data and the unstructured nature of code- mixed data are major challenges. To address these challenges, we explored various tech- niques, including Machine Learning models such as Decision Trees, Random Forests, Lo- gistic Regression, and Gaussian Na ̈ıve Bayes, Deep Learning model, such as Long Short- Term Memory (LSTM), and Transfer Learning model like BERT, were also utilized. In this work, we obtained the dataset from the Dravid- ianLangTech shared task by participating in a competition and accessing train, development and test data for Tamil Language. The results demonstrated promising performance in senti- ment analysis of code-mixed text. Among all the models, deep learning model LSTM pro- vides best accuracy of 0.61 for Tamil language.</abstract>
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%0 Conference Proceedings
%T KEC_AI_NLP@DravidianLangTech: Sentiment Analysis in Code Mixture Language
%A Shanmugavadivel, Kogilavani
%A Subaramanian, Malliga
%A S, VetriVendhan
%A M, Pramoth Kumar
%A S, Karthickeyan
%A N, Kavin Vishnu
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F shanmugavadivel-etal-2023-kec-ai
%X Sentiment Analysis is a process that involves analyzing digital text to determine the emo- tional tone, such as positive, negative, neu- tral, or unknown. Sentiment Analysis of code- mixed languages presents challenges in natural language processing due to the complexity of code-mixed data, which combines vocabulary and grammar from multiple languages and cre- ates unique structures. The scarcity of anno- tated data and the unstructured nature of code- mixed data are major challenges. To address these challenges, we explored various tech- niques, including Machine Learning models such as Decision Trees, Random Forests, Lo- gistic Regression, and Gaussian Na ̈ıve Bayes, Deep Learning model, such as Long Short- Term Memory (LSTM), and Transfer Learning model like BERT, were also utilized. In this work, we obtained the dataset from the Dravid- ianLangTech shared task by participating in a competition and accessing train, development and test data for Tamil Language. The results demonstrated promising performance in senti- ment analysis of code-mixed text. Among all the models, deep learning model LSTM pro- vides best accuracy of 0.61 for Tamil language.
%U https://aclanthology.org/2023.dravidianlangtech-1.44
%P 300-305
Markdown (Informal)
[KEC_AI_NLP@DravidianLangTech: Sentiment Analysis in Code Mixture Language](https://aclanthology.org/2023.dravidianlangtech-1.44) (Shanmugavadivel et al., DravidianLangTech-WS 2023)
ACL
- Kogilavani Shanmugavadivel, Malliga Subaramanian, VetriVendhan S, Pramoth Kumar M, Karthickeyan S, and Kavin Vishnu N. 2023. KEC_AI_NLP@DravidianLangTech: Sentiment Analysis in Code Mixture Language. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 300–305, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.