Generating and visualizing topic hierarchies from microblogs: An iterative latent dirichlet allocation approach

Research in social networks is attaining more attention in the recent past due to the explosive growth in the creation and sharing of information over social media. As the volume of information grows exponentially, we need efficient computational techniques to analyze this information and to synthesis the hidden knowledge associated with it. Being a suit of text understanding algorithms, topic modeling discovers the topics or themes within a huge collection of documents. In this work, we employ the essence of a powerful topic modeling algorithm to analyze hidden knowledge contained in the information spread across a famous social network platform Twitter, using a novel iterative topic modeling approach. Additionally, we visualized the knowledge extracted using a sunburst chart so that even a naive user can interpret the hidden knowledge extracted from tweets.