Sentiment and Emotion Analysis for Social Multimedia

Abstract

Online social networks have attracted the attention 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. 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 focuses on textual information regardless of the visual content, which may be as informative in expressing people's sentiments and opinions. In this research, we attempt to analyze the online sentiment changes of social media users using both the textual and visual content. Nowadays, social media networks such as Twitter have become major platforms of information exchange and communication between users, with tweets as the common information carrier. As an old saying has it, an image is worth a thousand words. The image tweet is a great example of multimodal sentiment. In this research, we focus on sentiment analysis based on visual and multimedia information analysis. We will review the state-of-the-art in this topic. Several of our projects related to this research area will also be discussed. Experimental results are included to demonstrate and summarize our contributions.