  Nowadays social media is a huge platform of data. People usually share their
interest, thoughts via discussions, tweets, status. It is not possible to go
through all the data manually. We need to mine the data to explore hidden
patterns or unknown correlations, find out the dominant topic in data and
understand people's interest through the discussions. In this work, we explore
Twitter data related to health. We extract the popular topics under different
categories (e.g. diet, exercise) discussed in Twitter via topic modeling,
observe model behavior on new tweets, discover interesting correlation (i.e.
Yoga-Veganism). We evaluate accuracy by comparing with ground truth using
manual annotation both for train and test data.
