Sentiment community detection: exploring sentiments and relationships in social networks

This study introduces the concept of “Sentiment Community,” a group of users who are closely connected and highly consistent in their sentiments about one product/service and proposes two methods to discover sentiment communities by adopting the optimization models of semi-definite programming (SDP).

Abstract

Abstract Social networking sites (SNS), which allow users to express opinions on products/services, have become an important channel and platform for enterprises to acquire and trace users’ sentiments in order to design appropriate business strategies and online marketing campaigns. However, with the large number of users and complex user relationships on SNS, effectively capturing these sentiments for business decision support is still a big challenge. In this study we introduce the concept of “Sentiment Community, ” a group of users who are closely connected and highly consistent in their sentiments about one product/service. Discovering such sentiment communities would be very valuable to enterprises for customer segmentation and target marketing. Taking into account both connections and sentiments, we propose two methods to discover sentiment communities by adopting the optimization models of semi-definite programming (SDP). Our experimental evaluations demonstrated great performances for the proposed methods. This study opens the doors to effectively explore users’ sentiments on SNS for business decision making.