Version 1
: Received: 18 June 2023 / Approved: 19 June 2023 / Online: 19 June 2023 (09:33:58 CEST)
How to cite:
Dang, C.; Moreno-García, M.N.; De la Prieta, F.; Nguyen, K.V.; Ngo, V.M. Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models. Preprints2023, 2023061318. https://doi.org/10.20944/preprints202306.1318.v1
Dang, C.; Moreno-García, M.N.; De la Prieta, F.; Nguyen, K.V.; Ngo, V.M. Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models. Preprints 2023, 2023061318. https://doi.org/10.20944/preprints202306.1318.v1
Dang, C.; Moreno-García, M.N.; De la Prieta, F.; Nguyen, K.V.; Ngo, V.M. Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models. Preprints2023, 2023061318. https://doi.org/10.20944/preprints202306.1318.v1
APA Style
Dang, C., Moreno-García, M.N., De la Prieta, F., Nguyen, K.V., & Ngo, V.M. (2023). Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models. Preprints. https://doi.org/10.20944/preprints202306.1318.v1
Chicago/Turabian Style
Dang, C., Kien V. Nguyen and Vuong M. Ngo. 2023 "Sentiment Analysis for Vietnamese – Based Hybrid Deep Learning Models" Preprints. https://doi.org/10.20944/preprints202306.1318.v1
Abstract
Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language. Our research questions are to determine whether it is possible to produce hybrid models that outperform the Vietnamese language. Hybrid deep sentiment-analysis learning models are built and tested on reviews and feedback of the Vietnamese language. The hybrid models outperformed the accuracy of Vietnamese sentiment analysis on Vietnamese datasets. It contributes to the growing body of research on Vietnamese NLP, providing insights and directions for future studies in this area.
Keywords
Hybrid Deep Learning Models; Sentiment Analysis; Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
