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

Sena Al-Hadhrami (Corresponding / Lead Author), Tamas Vinko, Tawfik Al-Hadhrami*, Faisal Saeed (Corresponding / Lead Author), Sultan Noman Qasem

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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 LSTMCNN, for sentiment analysis of patients’ drug reviews.
    Original languageEnglish
    Article numbere1976
    Pages (from-to)1-29
    Number of pages29
    JournalPeerJ Computer Science
    Issue number2024
    Publication statusPublished (VoR) - 29 Apr 2024


    • Algorithms and Analysis of Algorithms
    • Artificial Intelligence
    • Bi-LSTM-CNN
    • Bidirectional LSTM-CNN
    • CNN
    • Data Mining and Machine Learning
    • Data Science
    • Deep learning
    • LSTM
    • Neural Networks
    • Patients’ drug reviews
    • Sentiment analysis


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