EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets

Abstract

Spectators are increasingly using social platforms to express their opinions and share their emotions during big public events. Those reactions reveal the subjective perception of the event and extend its understanding. This has motivated us to develop a system to explore and visualize volume, patterns, and trends of user sentiments as they evolve over time.  Previous work in sentiment analysis and opinion mining has addressed these issues. But the majority of them distinguish only two polarity categories, leaving a more detailed and insightful analysis to be desired.  In this paper, we suggest using a fine-grained, multi-category emotion model to classify and visualize users' emotional reactions in public events. We describe EmotionWatch, a tool that constructs visual summaries of public emotions, and apply it to the 2012 Olympics as a test case. We report findings from a user study evaluating the usability of the tool and validating the emotion model. Results show that users prefer a more detailed inspection of public emotions over the simplified analysis. Despite its complexity, users were able to effectively grasp, understand, and interpret the emotional reactions using EmotionWatch. The same user study also pointed out few design improvements for the future development of analogous systems.