Sentiment Visualization on Tweet Stream
Hua Jin, Yatao Zhu, Zhiqiang Jin, Sandhya Arora · Journal of Software · 2014
Advancements in mobile technology and the proliferation of social media platforms have made it possible for individuals to stay constantly connected with friends and family.This has provided new opportunities to the emergency response domain, where the information shared by individuals in crisis can provide invaluable insight into the situation on the ground.Information shared on social media is highly dynamic, heterogeneous, large scale, geographically distributed and multilingual.Moreover, the context of such information is mostly relevant for a very short period of time and the information can be very subjective, embedded in personal feelings.This is a significant challenge for the emergency response domain, where critical decisions need to be made quickly on the basis of the users' situation awareness.We propose to address this issue using visual analytics techniques to facilitate browsing and understanding of topicality and feelings in social media.Our approach is twofold-firstly, we enrich social media posts by adding semantics to facilitate browsing and sentiment in order to gauge the emotions behind individual posts.Secondly, we combine two paradigms of data browsing -topical and temporal into a real-time dynamic visualisation of social media messages.