Managing Healthcare Infodemic by deep learning in providing healthcare services

Abstract

Digital Health care data acquisition and processing is performed by Artificial Intelligence and Internet of Things technologies and digitization of data and information affects the patients’ behavior. News about COVID-19, a global pandemic, is circulating on social media worldwide providing a collection of big data. Awareness about the pandemic is spreading drastically in the form of messages, social media posts, tweets, and videos. It is, therefore, significant to assess the early flow of information on social media during the pandemic to prevent alarmism. This study aims to perform sentiment analysis of social media big data about COVID-19 by deep learning on a dataset provided by IEEE Data Port. The goal is to assist healthcare professionals in developing social media policies that can be used to change public opinion. The Dataset used consists of 11,858 COVID-19-related tweets collected on May 30, 2020. Data are labeled as positive or negative in the first step using TextBlob and VADER. In step II, various machine learning models are compared using three feature extraction techniques in combination with VADER and TextBlob. The results show that Extra Tree Classifier using TF-IDF features outperforms with an accuracy of 0.9474.