Exploring Drug Sentiment Analysis with Machine Learning Techniques

This research investigates the use of machine learning techniques for sentiment analysis of drug reviews, which is a crucial task for extracting valuable information from the vast amount of unstructured data available online. Drug reviews provide patients' opinions on various aspects of drugs, including their efficacy, side effects, and overall experience. The study explores the performance of different machine learning algorithms for sentiment analysis of drug reviews. Additionally, it examines the effectiveness of various feature engineering techniques, such as bag-of-words, n-grams, and word embeddings, in capturing the subtleties of drug reviews. The study evaluates the performance of these techniques on a publicly available dataset of drug reviews and compares the results based on accuracy, precision, recall, and F1-score. The experiments demonstrate that the appropriate use of machine learning algorithms and feature engineering techniques can accurately capture the sentiment of drug reviews, achieving high accuracy and F1-score. This study's findings can provide valuable insights for healthcare professionals and researchers to analyze patient opinions about drugs, identify potential side effects, and enhance drug development and regulatory processes.