Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework
Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision making technique in which users provide a document based relevance feedback that allows the framework to converge to a user endorsed topic distribution. We also report feedback from a two stage study which shows that our technique results in topic model quality improvements on two independent measures.
IEEE Transactions on Visualization and Computer Graphics