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

Sena Al-Hadhrami; Tamas Vinko; Tawfik Al-Hadhrami; Faisal Saeed; Sultan Noman Qasem

doi:10.7717/peerj-cs.1976

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

PeerJ Comput Sci. 2024 Apr 29:10:e1976. doi: 10.7717/peerj-cs.1976. eCollection 2024.

Authors

Sena Al-Hadhrami¹, Tamas Vinko¹, Tawfik Al-Hadhrami², Faisal Saeed³, Sultan Noman Qasem⁴

Affiliations

¹ Institute of Informatics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary.
² Computer Science Department, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom.
³ DAAI Researsh Group, College of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom.
⁴ Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

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.

Keywords: Bi-LSTM-CNN; Bidirectional LSTM-CNN; CNN; Deep learning; LSTM; Patients’ drug reviews; Sentiment analysis.

Grants and funding

This work is funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) through Research Partnership Program no RP-21-07-09. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.