Enhancing Drug Reviews Insights through Exploratory Data Analysis and Sentiment Analysis

Matilde Pato

Abstract: The increasing volume of user-generated content across various online platforms has created vast datasets in multiple domains, including healthcare. This article explores the significant roles of data visualisation and sentiment analysis within the healthcare sector using the UCI ML Drug Review dataset. Our study highlights the value of exploratory data analysis and sentiment analysis in comprehending patient feedback, enriching insights from the dataset. Data visualisation effectively elucidates the data's distribution and key characteristics, while sentiment analysis, performed using TextBlob and VADER, categorises the emotional tone of patient reviews. Our methodology aims to provide a deeper understanding of patient satisfaction and medication efficacy based on user-generated content.