#### Location

Online

#### Event Website

https://hicss.hawaii.edu/

#### Start Date

4-1-2021 12:00 AM

#### End Date

9-1-2021 12:00 AM

#### Description

This paper describes a method for constructing a causality model from review text data. Review text data include the evaluation factors of rating, and causality model extraction from text data is important for understanding the evaluation factors and their relationships. Several methods are available for extracting causality models by using a topic model. In particular, the method based on hierarchical latent Dirichlet allocation is useful for hierarchically comprehending causality structure. However, the depth of each topic in a hierarchical structure is forcefully pruned even if granularities differ for each topic. Thus, interpreting a hierarchical topic structure is difficult. To solve these problems, we construct a hierarchical topic structure with different depths by using Bayesian rose trees. Furthermore, we use conceptual labeling to add explicit semantics for each topic for interpretation. An experiment confirms that this model is accurate and interpretable using actual data.

Text-based Causality Modeling with a Conceptual Label in a Hierarchical Topic Structure Using Bayesian Rose Trees

Online

This paper describes a method for constructing a causality model from review text data. Review text data include the evaluation factors of rating, and causality model extraction from text data is important for understanding the evaluation factors and their relationships. Several methods are available for extracting causality models by using a topic model. In particular, the method based on hierarchical latent Dirichlet allocation is useful for hierarchically comprehending causality structure. However, the depth of each topic in a hierarchical structure is forcefully pruned even if granularities differ for each topic. Thus, interpreting a hierarchical topic structure is difficult. To solve these problems, we construct a hierarchical topic structure with different depths by using Bayesian rose trees. Furthermore, we use conceptual labeling to add explicit semantics for each topic for interpretation. An experiment confirms that this model is accurate and interpretable using actual data.

https://aisel.aisnet.org/hicss-54/da/data_text_web_mining/7