How Positive Sentiment of Telemedicine Applications using Naïve Bayes and Support Vector Machine?

Abstract

The telemedicine software lets users schedule appointments with physicians or hospitals, purchase medications online, read health-related news and publications, and have online consultations with doctors. Public opinion is necessary for this application to meet societal needs. The public's perception of contemporary health applications can be ascertained by looking to public opinion. Fine-grained sentiment analysis is used in public opinion taking to extract information from evaluations posted by the public and categorize it into positive and negative groups. Using RapidMiner as a supporting tool, sentiment analysis uses the Naïve Bayes Classification algorithm and Support Vector Machine to determine an accuracy value. After analyzing data from two different sources—Twitter and Playstore—it can be said that the Support Vector Machine algorithm performs 90.99% more accurately in predicting public opinion than the Naïve Bayes algorithm, which achieves 88.55% accuracy.

Keywords

• Fine-Grained Sentiment Analysis
• Naïve Bayes
• Support Vector Machine
• Telemedicine