Verification of Model Modification Operations in Interactive Topic Modeling based on GDM

Abstract

This paper proposes an interactive topic modeling based on GDM (Geometric Dirichlet Means) and verifies its effectiveness by applying it to a news corpus. Topic modeling is a method for probabilistically analyzing latent topics in a set of documents. As it is an unsupervised learning, it may produce results that an analyst does not intend. To solve this problem, this paper introduces the concept of Human-in-the-Loop to obtain topics corresponding to the analyst's intention by incorporating the analyst's knowledge into the learning process. The proposed method employs GDM, which is based on geometric computation and has a high affinity with a document clustering. Model change operations with the parameters to be adjusted are defined, of which the effectiveness is shown with a verification experiment.