Summarization and sentiment analysis from user health posts

Online health communities continue to offer huge variety of medical information useful for medical practitioners, system administrators and patients alike. In this work we collect real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them. We propose to perform Summarization of user posts per drug, and come out with useful conclusions for medical fraternity as well as patient community at a glance. Further, we propose to classify the users based on their 'emotional state of mind'. Also, we shall perform knowledge discovery from user posts, whereby useful 'patterns' about the triad 'drugs-symptoms-medicine' is done by Association Rule Mining.