@inproceedings{balaji-etal-2024-wordwizards,
title = "{W}ord{W}izards@{D}ravidian{L}ang{T}ech 2024: Sentiment Analysis in {T}amil and {T}ulu using Sentence Embedding",
author = "Balaji, Shreedevi and
Anbalagan, Akshatha and
T, Priyadharshini and
A, Niranjana and
Thenmozhi, Durairaj",
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-1.36",
pages = "218--222",
abstract = "Sentiment Analysis of Dravidian Languages has begun to garner attention recently as there is more need to analyze emotional responses and subjective opinions present in social media text. As this data is code-mixed and there are not many solutions to code-mixed text out there, we present to you a stellar solution to DravidianLangTech 2024: Sentiment Analysis in Tamil and Tulu task. To understand the sentiment of social media text, we used pre-trained transformer models and feature extraction vectorizers to classify the data with results that placed us 11th in the rankings for the Tamil task and 8th for the Tulu task with a accuracy F1 score of 0.12 and 0.30 which shows the efficiency of our approach.",
}
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<abstract>Sentiment Analysis of Dravidian Languages has begun to garner attention recently as there is more need to analyze emotional responses and subjective opinions present in social media text. As this data is code-mixed and there are not many solutions to code-mixed text out there, we present to you a stellar solution to DravidianLangTech 2024: Sentiment Analysis in Tamil and Tulu task. To understand the sentiment of social media text, we used pre-trained transformer models and feature extraction vectorizers to classify the data with results that placed us 11th in the rankings for the Tamil task and 8th for the Tulu task with a accuracy F1 score of 0.12 and 0.30 which shows the efficiency of our approach.</abstract>
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%0 Conference Proceedings
%T WordWizards@DravidianLangTech 2024: Sentiment Analysis in Tamil and Tulu using Sentence Embedding
%A Balaji, Shreedevi
%A Anbalagan, Akshatha
%A T, Priyadharshini
%A A, Niranjana
%A Thenmozhi, Durairaj
%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 balaji-etal-2024-wordwizards
%X Sentiment Analysis of Dravidian Languages has begun to garner attention recently as there is more need to analyze emotional responses and subjective opinions present in social media text. As this data is code-mixed and there are not many solutions to code-mixed text out there, we present to you a stellar solution to DravidianLangTech 2024: Sentiment Analysis in Tamil and Tulu task. To understand the sentiment of social media text, we used pre-trained transformer models and feature extraction vectorizers to classify the data with results that placed us 11th in the rankings for the Tamil task and 8th for the Tulu task with a accuracy F1 score of 0.12 and 0.30 which shows the efficiency of our approach.
%U https://aclanthology.org/2024.dravidianlangtech-1.36
%P 218-222
Markdown (Informal)
[WordWizards@DravidianLangTech 2024: Sentiment Analysis in Tamil and Tulu using Sentence Embedding](https://aclanthology.org/2024.dravidianlangtech-1.36) (Balaji et al., DravidianLangTech-WS 2024)
ACL