  Social Media has seen a tremendous growth in the last decade and is
continuing to grow at a rapid pace. With such adoption, it is increasingly
becoming a rich source of data for opinion mining and sentiment analysis. The
detection and analysis of sentiment in social media is thus a valuable topic
and attracts a lot of research efforts. Most of the earlier efforts focus on
supervised learning approaches to solve this problem, which require expensive
human annotations and therefore limits their practical use. In our work, we
propose a semi-supervised approach to predict user-level sentiments for
specific topics. We define and utilize a heterogeneous graph built from the
social networks of the users with the knowledge that connected users in social
networks typically share similar sentiments. Compared with the previous works,
we have several novelties: (1) we incorporate the influences/authoritativeness
of the users into the model, 2) we include comment-based and like-based
user-user links to the graph, 3) we superimpose multiple heterogeneous graphs
into one thereby allowing multiple types of links to exist between two users.
