A Survey On Interactivity in Topic Models

Abstract

Trying to make sense and gain deeper insight from large sets of data is becoming a task very central to computer science in general. Topic models, capable of uncovering the semantic themes pervading through large collections of documents, have seen a surge in popularity in recent years. However, topic models are high level statistical tools; their output is given in terms of probability distributions, suited neither for simple interpretation nor deep analysis. Interpreting the fitted topic models in an intuitive manner requires visual and interactive tools. Additionally, some measure of human interaction is typically required for refining the output offered by such models. In the research, this area remains relatively unexplored - only recently has this aspect been receiving more attention. In this paper, the literature is surveyed as it pertains to interactivity and visualisation within the context of topic models, with the goal of finding current research trends in this area.