Exploiting sentiment homophily for link prediction

Link prediction on social media is an important problem for recommendation systems. Understanding the interplay of users' sentiments and social relationships can be potentially valuable. Specifically, we study how to exploit sentiment homophily for link prediction. We evaluate our approach on a dataset gathered fro Twitter that consists of tweets sent in one month during U.S. 2012 political campaign along with the "follows" relationship between users. Our first contribution is defining a set of sentiment-based features that help predict the likelihood of two users becoming "friends" (i.e., mutually mentioning or following each other) based on their sentiments toward topics of mutual interest. Our evaluation in a supervised learning framework demonstrates the benefits of sentiment-based features in link prediction. We find that Adamic-Adar and Euclidean distance measures are the best predictors. Our second contribution is proposing a factor graph model that incorporates a sentiment-based variant of cognitive balance theory. Our evaluation shows that, when tie strength is not too weak, our model is more effective in link prediction than traditional machine learning techniques.