Health Monitoring on Social Media over Time using K-Means Clustering Algorithm

Abstract

The electronic application helps in spreading news, talking about social issues, and so forth and it got its own stand-out spot in our life. By utilizing the fast improvement of the electronic application we attempt to examine the soundness of the social requests by utilizing the tweets/posts in the online application. Early observing of welfare information is identifying with post-factum thinks about and empowers a degree of employment, for example, surveying social hazard factors and impelling welfare attempts. We portray two issues: welfare change zone and welfare advance conjecture. We utilize Temporal Ailment Topic Aspect Model (TM–ATAM). This procedure is utilized to withdraw the welfare-related tweets from various tweets.