Causality model for text data with a hierarchical topic structure

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 amo...

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Main Authors:	Takuro Ogawa, Hideyasu Shimadzu, Ryosuke Saga
Format:	Default Conference proceeding
Published:	2021
Subjects:	Bayesian rose trees Causality analysis Topic model Hierarchical topic structure
Online Access:	https://hdl.handle.net/2134/13203674.v1
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id	rr-article-13203674
record_format	Figshare
spelling	rr-article-132036742021-03-23T00:00:00Z Causality model for text data with a hierarchical topic structure Takuro Ogawa (9612653) Hideyasu Shimadzu (2567326) Ryosuke Saga (9612656) Bayesian rose trees Causality analysis Topic model Hierarchical topic structure 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. 2021-03-23T00:00:00Z Text Conference contribution 2134/13203674.v1 https://figshare.com/articles/conference_contribution/Causality_model_for_text_data_with_a_hierarchical_topic_structure/13203674 All Rights Reserved
institution	Loughborough University
collection	Figshare
topic	Bayesian rose trees Causality analysis Topic model Hierarchical topic structure
spellingShingle	Bayesian rose trees Causality analysis Topic model Hierarchical topic structure Takuro Ogawa Hideyasu Shimadzu Ryosuke Saga Causality model for text data with a hierarchical topic structure
description	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.
format	Default Conference proceeding
author	Takuro Ogawa Hideyasu Shimadzu Ryosuke Saga
author_facet	Takuro Ogawa Hideyasu Shimadzu Ryosuke Saga
author_sort	Takuro Ogawa (9612653)
title	Causality model for text data with a hierarchical topic structure
title_short	Causality model for text data with a hierarchical topic structure
title_full	Causality model for text data with a hierarchical topic structure
title_fullStr	Causality model for text data with a hierarchical topic structure
title_full_unstemmed	Causality model for text data with a hierarchical topic structure
title_sort	causality model for text data with a hierarchical topic structure
publishDate	2021
url	https://hdl.handle.net/2134/13203674.v1
_version_	1797822066340134912

Causality model for text data with a hierarchical topic structure

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