Analysis of Machine Learning and Deep Learning Frameworks for Opinion Mining on Drug Reviews. (30th June 2021)

• Record Type: Journal Article
• Authors: Youbi, Fatiha; Settouti, Nesma
• Is Part Of: Computer journal. Volume 65:Number 9(2022)
• Page Start: 2470
• Page End: 2483
• Publication Date: 2021-06-30
• Subjects: opinion mining -- deep learning -- drug reviews -- Word2vec -- CNN -- LSTM -- BLSTM -- GloVe -- natural language processing; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxab084
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Opinion mining from medical forums such as health check-ups is sparking growing interest and a stimulating area for natural language processing. This allows for a better understanding of patient health status and drug reactions while generating new knowledge for health care professionals and drug manufacturers, which helps improve the quality of service and produce more effective treatments. In this paper, the researchers present a framework of opinions classification of drug reviews. The objective of this work is to find the best model for analyzing patients' emotions about drugs. In this sense, the researchers oppose classical text vectorization methods (bag of words, term frequency-inverse document frequency) and word embedding methods (Word2vec, GloVe) for classical opinion mining face to modern machine learning tools with the Convolutional Neural Network (CNN), the Recurrent Neural Networks (Long Short-term Memory and Bidirectional Long Short-Term Memory). Experiments results show that the best model for drug reviews was achieved by CNN based on the Skip-gram model (85% accuracy). Experiments have led to conclude that the performance of a given model will depend on the type of dataset used, on feature representation and better collaboration between classifiers and feature extraction methods.