"Hey..! This medicine made me sick": Sentiment Analysis of User-Generated Drug Reviews using Machine Learning Techniques

TL;DR

This paper develops a machine learning-based system for sentiment analysis of user-generated drug reviews, utilizing pre-trained language models and classifiers to accurately categorize reviews as positive, negative, or neutral.

Contribution

It introduces a novel approach combining BERT-based embeddings with various classifiers for drug review sentiment analysis, validated on manually labeled public data.

Findings

Support vector machines achieved high precision and recall.

Pre-trained language models improved classification accuracy.

Deep learning models outperformed traditional classifiers.

Abstract

Sentiment analysis has become increasingly important in healthcare, especially in the biomedical and pharmaceutical fields. The data generated by the general public on the effectiveness, side effects, and adverse drug reactions are goldmines for different agencies and medicine producers to understand the concerns and reactions of people. Despite the challenge of obtaining datasets on drug-related problems, sentiment analysis on this topic would be a significant boon to the field. This project proposes a drug review classification system that classifies user reviews on a particular drug into different classes, such as positive, negative, and neutral. This approach uses a dataset that is collected from publicly available sources containing drug reviews, such as drugs.com. The collected data is manually labeled and verified manually to ensure that the labels are correct. Three pre-trained language models, such as BERT, SciBERT, and BioBERT, are used to obtain embeddings, which were later used as features to different machine learning classifiers such as decision trees, support vector machines, random forests, and also deep learning algorithms such as recurrent neural networks. The performance of these classifiers is quantified using precision, recall, and f1-score, and the results show that the proposed approaches are useful in analyzing the sentiments of people on different drugs.