LDA-based path model construction process for structure equation modeling

This paper proposes a novel approach to generate and analyze path model by structure equation modeling (SEM). SEM is an important technique to carry out causal analysis based on path model. As such, constructing path models, which result in reliable analysis, are important in SEM. LSA-based method, which is used to build a path model from text data, is proposed. However, this method requires each document to belong to one topic; thus, the model cannot express natural variables and relationships. Therefore, this paper extends the existing approach to latent Dirichlet allocation (LDA) and generates a path model from the extracted topics by LDA. Experiments using review text data can confirm the feasibility and applicability of the proposed process.