A machine learning approach on analysing the sentiments in the adoption of telemedicine application during COVID-19

Abstract

Purpose

The purpose of this paper is to understand the customer sentiment towards telemedicine apps and also to apply machine learning algorithms to analyse the sentiments in the adoption during the COVID-19 pandemic.

Design/methodology/approach

Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like support vector machine (SVM) and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix.

Findings

The paper explores the customer sentiment towards telemedicine apps and their adoption during the COVID-19 pandemic. Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like SVM and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix. The customers who used telemedicine apps have positive sentiment as well as negative sentiment towards the telemedicine apps. Some of the customers have concerns about the medicines delivered, their delivery time, the quality of service and other technical difficulties. Even a small percentage of doctors feel uncomfortable in online consultation through the application.

Originality/value

The primary value of this paper lies in providing an overview of the customers’ approach towards the telemedicine apps, especially during the COVID-19 pandemic.

Citation

M., D. and S., S. (2022), "A machine learning approach on analysing the sentiments in the adoption of telemedicine application during COVID-19", Journal of Science and Technology Policy Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JSTPM-01-2022-0017