@inproceedings{k-etal-2023-mucs,
title = "{MUCS}@{D}ravidian{L}ang{T}ech2023: Sentiment Analysis in Code-mixed {T}amil and {T}ulu Texts using fast{T}ext",
author = "K, Rachana and
M, Prajnashree and
Hegde, Asha and
Shashirekha, H. L",
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-1.38",
pages = "258--265",
abstract = "Sentiment Analysis (SA) is a field of computational study that focuses on analyzing and understanding people{'}s opinions, attitudes, and emotions towards an entity. An entity could be an individual, an event, a topic, a product etc., which is most likely to be covered by reviews and such reviews can be found in abundance on social media platforms. The increase in the number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. However, SA of social media text is challenging due to the complex nature of the code-mixed text. To tackle this issue, in this paper, we team MUCS, describe learning models submitted to {``}Sentiment Analysis in Tamil and Tulu{''} -DravidianLangTech@Recent Advances In Natural Language Processing (RANLP) 2023. Using fastText embeddings to train the Machine Learning (ML) models to perform SA in code-mixed Tamil and Tulu texts, the proposed methodology exhibited F1 scores of 0.14 and 0.204 securing 13th and 15th rank for Tamil and Tulu texts respectively.",
}
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<abstract>Sentiment Analysis (SA) is a field of computational study that focuses on analyzing and understanding people’s opinions, attitudes, and emotions towards an entity. An entity could be an individual, an event, a topic, a product etc., which is most likely to be covered by reviews and such reviews can be found in abundance on social media platforms. The increase in the number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. However, SA of social media text is challenging due to the complex nature of the code-mixed text. To tackle this issue, in this paper, we team MUCS, describe learning models submitted to “Sentiment Analysis in Tamil and Tulu” -DravidianLangTech@Recent Advances In Natural Language Processing (RANLP) 2023. Using fastText embeddings to train the Machine Learning (ML) models to perform SA in code-mixed Tamil and Tulu texts, the proposed methodology exhibited F1 scores of 0.14 and 0.204 securing 13th and 15th rank for Tamil and Tulu texts respectively.</abstract>
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%0 Conference Proceedings
%T MUCS@DravidianLangTech2023: Sentiment Analysis in Code-mixed Tamil and Tulu Texts using fastText
%A K, Rachana
%A M, Prajnashree
%A Hegde, Asha
%A Shashirekha, H. L.
%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 k-etal-2023-mucs
%X Sentiment Analysis (SA) is a field of computational study that focuses on analyzing and understanding people’s opinions, attitudes, and emotions towards an entity. An entity could be an individual, an event, a topic, a product etc., which is most likely to be covered by reviews and such reviews can be found in abundance on social media platforms. The increase in the number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. However, SA of social media text is challenging due to the complex nature of the code-mixed text. To tackle this issue, in this paper, we team MUCS, describe learning models submitted to “Sentiment Analysis in Tamil and Tulu” -DravidianLangTech@Recent Advances In Natural Language Processing (RANLP) 2023. Using fastText embeddings to train the Machine Learning (ML) models to perform SA in code-mixed Tamil and Tulu texts, the proposed methodology exhibited F1 scores of 0.14 and 0.204 securing 13th and 15th rank for Tamil and Tulu texts respectively.
%U https://aclanthology.org/2023.dravidianlangtech-1.38
%P 258-265
Markdown (Informal)
[MUCS@DravidianLangTech2023: Sentiment Analysis in Code-mixed Tamil and Tulu Texts using fastText](https://aclanthology.org/2023.dravidianlangtech-1.38) (K et al., DravidianLangTech-WS 2023)
ACL