Enhancing Drug Reviews Insights through Exploratory Data Analysis and Sentiment Analysis

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 data...

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Bibliographic Details
Published in2024 28th International Conference Information Visualisation (IV) pp. 190 - 195
Main Authors Pinto, Ana Sofia, Pato, Matilde, Datia, Nuno
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.07.2024
Subjects
Data analysis
Data visualization
Drug Recommendation System
Drugs
Exploratory Data Analysis
Healthcare Support
Medical services
NLP
Reviews
Sentiment analysis
User-generated content
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Summary: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.
ISSN:2375-0138
DOI:10.1109/IV64223.2024.00042