@inproceedings{yigezu-etal-2023-habesha,
title = "Habesha@{D}ravidian{L}ang{T}ech: Utilizing Deep and Transfer Learning Approaches for Sentiment Analysis.",
author = "Yigezu, Mesay Gemeda and
Kebede, Tadesse and
Kolesnikova, Olga and
Sidorov, Grigori and
Gelbukh, Alexander",
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-1.35",
pages = "239--243",
abstract = "This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.",
}
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<abstract>This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.</abstract>
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%0 Conference Proceedings
%T Habesha@DravidianLangTech: Utilizing Deep and Transfer Learning Approaches for Sentiment Analysis.
%A Yigezu, Mesay Gemeda
%A Kebede, Tadesse
%A Kolesnikova, Olga
%A Sidorov, Grigori
%A Gelbukh, Alexander
%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 yigezu-etal-2023-habesha
%X This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.
%U https://aclanthology.org/2023.dravidianlangtech-1.35
%P 239-243
Markdown (Informal)
[Habesha@DravidianLangTech: Utilizing Deep and Transfer Learning Approaches for Sentiment Analysis.](https://aclanthology.org/2023.dravidianlangtech-1.35) (Yigezu et al., DravidianLangTech-WS 2023)
ACL