2020Ogawa.pdf (511.4 kB)
Download fileCausality model for text data with a hierarchical topic structure
conference contribution
posted on 2020-11-09, 12:07 authored by Takuro Ogawa, Hideyasu ShimadzuHideyasu Shimadzu, Ryosuke SagaThis 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
Published in
2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)Pages
205-210Source
2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI 2020)Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2020-10-14Publication date
2021-03-23Copyright date
2020ISBN
9781665403801eISSN
2376-6824Publisher version
Language
- en