I. Introduction
Twitter currently has reached 336 million average active users every month [1]. It is the biggest live open data source for a variety of domain, some part of which has obvious clinical data for the healthcare industry. The value of data is grasped only when this raw information is converted into the knowledge that helps make decisions. Using data science strategy, healthcare organizations can benefit on increasing volumes of data and medical knowledge in an organized, strategic way. Also, individual clinicians can use that knowledge to improve the safety, quality, and efficiency of the care they provide. This project aims to analyze Twitter data and extract various informations like sentiment, prevalent location, trend, diseases, treatments, symptoms, etc. Also, it applies various natural language processing & machine learning technologies such as classification, entity recognition, etc. to gain insights into data related with healthcare domain.