  Web 2.0 helps to expand the range and depth of conversation on many issues
and facilitates the formation of online communities. Online communities draw
various individuals together based on their common opinions on a core set of
issues. Most existing community detection methods merely focus on discovering
communities without providing any insight regarding the collective opinions of
community members and the motives behind the formation of communities. Several
efforts have been made to tackle this problem by presenting a set of keywords
as a community profile. However, they neglect the positions of community
members towards keywords, which play an important role for understanding
communities in the highly polarized atmosphere of social media. To this end, we
present a sentiment-driven community profiling and detection framework which
aims to provide community profiles presenting positive and negative collective
opinions of community members separately. With this regard, our framework
initially extracts key expressions in users' messages as representative of
issues and then identifies users' positive/negative attitudes towards these key
expressions. Next, it uncovers a low-dimensional latent space in order to
cluster users according to their opinions and social interactions (i.e.,
retweets). We demonstrate the effectiveness of our framework through
quantitative and qualitative evaluations.
