Towards social imagematics

Abstract

Online social networks have attracted attention of people from both the academia and real world. In particular, the rich multimedia information accumulated in recent years provides an easy and convenient way for more active communication between people. This offers an opportunity to research people's behaviors and activities based on those multimedia content, which can be considered as social imagematics. One emerging area is driven by the fact that these massive multimedia data contain people's daily sentiments and opinions. However, existing sentiment analysis typically only pays attention to the textual information regardless of the visual content, which may be more informative in expressing people's sentiments and opinions. In this paper, we attempt to analyze the online sentiment changes of social media users using both the textual and visual content. In particular, we analyze the sentiment changes of Twitter users using both textual and visual features. An empirical study of real Twitter data sets indicates that the sentiments expressed in textual content and visual content are correlated. The preliminary results in this paper give insight into the important role of visual content in online social media.