SentiBubbles: Topic Modeling and Sentiment Visualization of Entity-centric Tweets
This work addresses the need for real-time sentiment and topic analysis of social media data for entities, but it is incremental as it applies existing methods to a specific domain.
The authors tackled the problem of aggregating entity-centric tweets daily to analyze reactions to news events, resulting in a visualization system that combines topic modeling and sentiment analysis to provide insights into current events and public sentiment.
Social Media users tend to mention entities when reacting to news events. The main purpose of this work is to create entity-centric aggregations of tweets on a daily basis. By applying topic modeling and sentiment analysis, we create data visualization insights about current events and people reactions to those events from an entity-centric perspective.