SentiView: Sentiment Analysis and Visualization for Internet Popular Topics

摘要

There would be value to several domains in discovering and visualizing sentiments in online posts. This paper presents SentiView, an interactive visualization system that aims to analyze public sentiments for popular topics on the Internet. SentiView combines uncertainty modeling and model-driven adjustment. By searching and correlating frequent words in text data, it mines and models the changes of the sentiment on public topics. In addition, using a time-varying helix together with an attribute astrolabe to represent sentiments, it can visualize the changes of multiple attributes and relationships among demographics of interest and the sentiments of participants on popular topics. The relationships of interest among different participants are presented in a relationship map. Using a new evolution model that is based on cellular automata, it is able to compare the time-varying features for sentiment-driven forums on both simulated and real data. Adaptable for different social networking platforms, such as Twitter, blog and forum, the methods demonstrate the effectiveness of SentiView in analyzing and visualizing public sentiments on the Web.