A sentiment-aware approach to community formation in social media

Abstract

Participating in a community exemplifies the aspect of sharing, networking and interacting in a social media system. There has been extensive work on characterising on-line communities by their contents and tags using topic modelling tools. However, the role of sentiment and mood has not been studied. Arguably, mood is an integral feature of a text, and becomes more significant in the context of social media: two communities might discuss precisely the same topics, yet within an entirely different atmosphere. Such sentiment-related distinctions are important for many kinds of analysis and applications, such as community recommendation. We present a novel approach to identification of latent hyper-groups in social communities based on users' sentiment. The results show that a sentiment-based approach can yield useful insights into community formation and metacommunities, having potential applications in, for example, mental health - by targeting support or surveillance to communities with negative mood - or in marketing - by targeting customer communities having the same sentiment on similar topics. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.