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Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach


Citation

Riyadi, Slamet and Mubarok, Muhammad Dzaki and Damarjati, Cahya and Ishak, Asnor Juraiza (2024) Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach. JUITA: Jurnal Informatika, 12 (2). pp. 159-167. ISSN 2579-8901; eISSN: 2086-9398

Abstract

Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep Learning achieves a high level of accuracy when applied to sentiment analysis in many domains. Nevertheless, no investigation has been conducted thus far utilizing deep learning techniques to examine emotion towards STY. This study employs modified Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), and CRNN models with and without data oversampling. The research findings indicate that the CRNN model, when combined with data oversampling and a redesigned architecture, achieves the highest level of accuracy (1.00) and consistently performs well. This research provides significant contributions in three areas: firstly, it utilizes Deep Learning techniques for sentiment analysis on STY; secondly, it modifies the CRNN architecture; and thirdly, it applies data oversampling to address the issue of imbalanced data.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.30595/juita.v12i2.21847
Publisher: Lembaga Publikasi Ilmiah dan Penerbitan Universitas Muhammadiyah Purwokerto
Keywords: Sentiment analysis; Deep learning; Overfitting; Oversampling; Imbalanced data
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 29 May 2025 02:27
Last Modified: 29 May 2025 02:27
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.30595/juita.v12i2.21847
URI: http://psasir.upm.edu.my/id/eprint/117530
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