  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.
