Sentiment analysis-based framework for assessing internet telemedicine videos

Abstract

Telemedicine services through internet and mobile devices need effective medical video delivery systems. This work describes a novel framework to study the assessment of internet-based telemedicine videos using sentiment analysis. The dataset comprises more than 1,000 text comments of medical experts collected from various medical animation videos of Youtube repository. The proposed framework deploys machine learning classifiers such as Bayes net, KNN, C 4.5 decision tree, support vector machine (SVM) and SVM with particle swarm optimisation (SVM-PSO) to infer opinion mining outputs. The results portray that SVM-PSO classifier performs better in assessing the reviews of medical video content with more than 80% accuracy. The model's inference of precision and recall values using SVM-PSO algorithm shows 87.8% and 85.57% respectively and henceforth underlines its superiority over other classifiers. The concepts of sentiment analysis can be applied effectively to the web-based user comments of medical videos and the end results can be highly critical to enhance the reputation of telemedicine education across the globe.