  Social media platforms contain a great wealth of information which provides
opportunities for us to explore hidden patterns or unknown correlations, and
understand people's satisfaction with what they are discussing. As one
showcase, in this paper, we present a system, TwiInsight which explores the
insight of Twitter data. Different from other Twitter analysis systems,
TwiInsight automatically extracts the popular topics under different categories
(e.g., healthcare, food, technology, sports and transport) discussed in Twitter
via topic modeling and also identifies the correlated topics across different
categories. Additionally, it also discovers the people's opinions on the tweets
and topics via the sentiment analysis. The system also employs an intuitive and
informative visualization to show the uncovered insight. Furthermore, we also
develop and compare six most popular algorithms - three for sentiment analysis
and three for topic modeling.
