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Causality model for text data with a hierarchical topic structure

conference contribution
posted on 09.11.2020, 12:07 by Takuro Ogawa, Hideyasu Shimadzu, Ryosuke Saga
This study describes a method for constructing a causality model from text data, such as review data. Topic modeling is useful to find these evaluation factors from text data. The method based on hierarchical latent Dirichlet allocation is useful because it automatically constructs relationships among topics. However, the depth of each topic in a hierarchical structure is the same even if the contents differ for each topic. Accordingly, the method can generate less important topics that are not worth analyzing. To solve this problem, we construct a hierarchical topic structure with different depths and more important topics by using Bayesian rose trees. In the experiment, the values of the hyperparameters for constructing a hierarchical topic structure are estimated by using evaluation indexes for causal analysis. In addition, the experiment compares the proposed method with related approaches to demonstrate the usefulness of this model.

History

School

  • Science

Department

  • Mathematical Sciences

Source

2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI 2020)

Version

AM (Accepted Manuscript)

Acceptance date

14/10/2020

Language

en

Location

Taipei, Taiwan

Event dates

3rd December 2020 - 5th December 2020

Depositor

Dr Hideyasu Shimadzu. Deposit date: 7 November 2020

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