Twitter Based Classification for Personal and Non-personal Heart Disease Claims

Abstract

The popularity of Twitter has created a massive social interaction between users that generates a large amount of data containing their opinions and feelings in different subjects including their health conditions, these data contain important information that can be used in disease monitoring and detection, therefore, Twitter has attracted the attention of many researchers as it has proven to be an important source of health information on the Internet. In this work, we conducted a systematic literature review to discover state-of-the-art methods used in the analysis of Twitter posts related to health, then we proposed an approach based on machine learning, sentiment analysis methods and Big Data technologies to ensure optimal classification of the health status of a population related to cardiovascular diseases in a Twitter environment.