Exploring Trending Topics of Social Media Text with VoronoiTopicCloud Provide Useful and Intuitive Insights into Social Media Texts

Enormous volume of data on social media platforms such as Weibo and Twitter is being generated every day. Microblogs collected from Weibo on a certain topic may consist of numerous conversation threads about relevant subtopics. However, it is difficult to distinguish these subtopics if the data is visualized as a single WordCloud because there’s no contextual information around the words in WordCloud. To help identify subtopics under a broader topic, we introduce VoronoiTopicCloud, a novel technique for visualizing the opinions in a large social media text collection. In order to overcome the problem of semantic sparseness and improve the performance of text clustering, we propose a novel corpus-based enrichment approach for short text. In the expansion procedure, new words which may not appear in the original short text document are added to the document with a virtual weight and the virtual weight is obtained from the posterior probabilities of new words given all the words in that document. At last, VoronoiTopicCloud is implemented as a lightweight application that runs in the browser.