Analysis of Machine Learning and Deep Learning Frameworks for Opinion Mining on Drug Reviews

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.

Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

“…When the data is textual, the extraction of features is a bit different where the aim is to create word or text embeddings. Generally in medical NLP and at the feature extraction level, the BERT (as a state-of-the-art model) was used in several studies as in [14] , [15] , [16] , [17] , whereas, the Word2Vec model was used in [18] , [19] , [20] . In contrast, and at the algorithmic level, various deep learning models were widely used in medical NLP.…”

“…In contrast, and at the algorithmic level, various deep learning models were widely used in medical NLP. Such as: The BiLSTM/LSTM in [14] , [16] , [20] , [21] , [22] , [23] , [24] , the convolutional neural network (CNN) in [19] , [25] , the capsule network [26] , the transformers [27] , the ResNet-34 network [28] , and the generative adversarial network (GAN) [29] . Medical symptom extraction is a well-known problem in health or medical-related NLP tasks.…”

Automatic Symptoms Identification from Massive Volume of Unstructured Medical Consultations Using Deep Neural and BERT Models

“…When the data is textual, the extraction of features is a bit different where the aim is to create word or text embeddings. Generally in medical NLP and at the feature extraction level, the BERT (as a state-of-the-art model) was used in several studies as in [14,15,16,17], whereas, the Word2Vec model was used in [18,19,20]. In contrast, and at the algorithmic level, various deep learning models were widely used in medical NLP.…”

“…In contrast, and at the algorithmic level, various deep learning models were widely used in medical NLP. Such as: The BiLSTM/LSTM in [14,16,20,21,22,23,24], the convolutional neural network (CNN) in [19,25], the capsule network [26], the transformers [27], the ResNet-34 network [28], and the generative adversarial network (GAN) [29]. Medical symptom extraction is a wellknown problem in health or medical-related NLP tasks.…”

Data Analytics in the Pharmacology Domain

“…Online resources such as Twitter and drug review applications enable patients to share feedback about their medications and check others' reviews (Gopalakrishnan & Ramaswamy, 2017). Moreover, patient feedback will help doctors make better decisions about prescriptions and improve drug quality (Youbi & Settouti, 2021).…”