Deep learning-based method for sentiment analysis for patients’ drug reviews

Al-Hadhrami, S, Vinko, T, Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X, Saeed, F and Qasem, SN, 2024. Deep learning-based method for sentiment analysis for patients’ drug reviews. PeerJ Computer Science, 10: e1976. ISSN 2376-5992

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Abstract

This article explores the application of deep learning techniques for sentiment analysis of patients’ drug reviews. The main focus is to evaluate the effectiveness of bidirectional long-short-term memory (LSTM) and a hybrid model (bidirectional LSTM-CNN) for sentiment classification based on the entire review text, medical conditions, and rating scores. This study also investigates the impact of using GloVe word embeddings on the model’s performance. Two different drug review datasets were used to train and test the models. The proposed methodology involves the implementation and evaluation of both deep learning models with the GloVe word embeddings for sentiment analysis of drug reviews. The experimental results indicate that Model A (Bi-LSTM-CNN) achieved an accuracy of 96% and Model B (Bi-LSTM-CNN) performs consistently at 87% for accuracy. Notably, the incorporation of GloVe word representations improves the overall performance of the models, as supported by Cohen’s Kappa coefficient, indicating a high level of agreement. These findings showed the efficacy of deep learning-based approaches, particularly bidirectional LSTM and bidirectional LSTM-CNN, for sentiment analysis of patients’ drug reviews.

Item Type:	Journal article
Publication Title:	PeerJ Computer Science
Creators:	Al-Hadhrami, S., Vinko, T., Al-Hadhrami, T., Saeed, F. and Qasem, S.N.
Publisher:	PeerJ
Date:	29 April 2024
Volume:	10
ISSN:	2376-5992
Identifiers:	Number Type 10.7717/peerj-cs.1976 DOI 1892768 Other
Rights:	This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	09 May 2024 08:16
Last Modified:	09 May 2024 08:16
URI:	https://irep.ntu.ac.uk/id/eprint/51413

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