Tea in benefits of health: A literature analysis using text mining and latent dirichlet allocation

Ching Hsue Cheng; Wei Lun Hung

doi:10.1145/3239438.3239459

Tea in benefits of health: A literature analysis using text mining and latent dirichlet allocation

Ching Hsue Cheng
, Wei Lun Hung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Tea originated in Asian, which was initially used as a medicinal herb. The variety of tea is according to different manufacturing processes and levels of oxidation. The different varieties of tea have different level of effects on health, thus this study adopted text mining technique and Latent Dirichlet Allocation (LDA) to analyze literature for tea in health effect. This study chose Web of Science as the database of literature source, and the search literature from 2007 to 2017. The total 1230 journal articles were collected in this study. The title, abstract, and keywords of the collected journal articles were used as a dataset for the experiment. Experimental results show that the VEM method is significantly lower than Gibbs sampling in perplexity. Hence, this study chooses K=150 when VEM method and Gibbs sampling reach the minimal perplexity in the same time. Many topics that related with tea and compounds of tea, however some topics had terms that related to health and disease. The top 10 topics show that tea could reduce the risk of diseases and benefit of health.

Original language	English
Title of host publication	ICMHI 2018 - Proceedings of 2018 the 2nd International Conference on Medical and Health Informatics
Publisher	Association for Computing Machinery (ACM)
Pages	148-155
Number of pages	8
ISBN (Electronic)	9781450363891
DOIs	https://doi.org/10.1145/3239438.3239459
Publication status	Published - Jun 8 2018
Externally published	Yes
Event	2nd International Conference on Medical and Health Informatics, ICMHI 2018 - Tsukuba, Japan Duration: Jun 8 2018 → Jun 10 2018

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2nd International Conference on Medical and Health Informatics, ICMHI 2018
Country/Territory	Japan
City	Tsukuba
Period	6/8/18 → 6/10/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Health
Latent Dirichlet Allocation
LDA
Literature analysis
Tea
Text mining
Topic model

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3239438.3239459

Cite this

Cheng, C. H., & Hung, W. L. (2018). Tea in benefits of health: A literature analysis using text mining and latent dirichlet allocation. In ICMHI 2018 - Proceedings of 2018 the 2nd International Conference on Medical and Health Informatics (pp. 148-155). (ACM International Conference Proceeding Series). Association for Computing Machinery (ACM). https://doi.org/10.1145/3239438.3239459

Tea in benefits of health: A literature analysis using text mining and latent dirichlet allocation. / Cheng, Ching Hsue; Hung, Wei Lun.
ICMHI 2018 - Proceedings of 2018 the 2nd International Conference on Medical and Health Informatics. Association for Computing Machinery (ACM), 2018. p. 148-155 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cheng, CH & Hung, WL 2018, Tea in benefits of health: A literature analysis using text mining and latent dirichlet allocation. in ICMHI 2018 - Proceedings of 2018 the 2nd International Conference on Medical and Health Informatics. ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), pp. 148-155, 2nd International Conference on Medical and Health Informatics, ICMHI 2018, Tsukuba, Japan, 6/8/18. https://doi.org/10.1145/3239438.3239459

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abstract = "Tea originated in Asian, which was initially used as a medicinal herb. The variety of tea is according to different manufacturing processes and levels of oxidation. The different varieties of tea have different level of effects on health, thus this study adopted text mining technique and Latent Dirichlet Allocation (LDA) to analyze literature for tea in health effect. This study chose Web of Science as the database of literature source, and the search literature from 2007 to 2017. The total 1230 journal articles were collected in this study. The title, abstract, and keywords of the collected journal articles were used as a dataset for the experiment. Experimental results show that the VEM method is significantly lower than Gibbs sampling in perplexity. Hence, this study chooses K=150 when VEM method and Gibbs sampling reach the minimal perplexity in the same time. Many topics that related with tea and compounds of tea, however some topics had terms that related to health and disease. The top 10 topics show that tea could reduce the risk of diseases and benefit of health.",

keywords = "Health, Latent Dirichlet Allocation, LDA, Literature analysis, Tea, Text mining, Topic model",

author = "Cheng, \{Ching Hsue\} and Hung, \{Wei Lun\}",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2nd International Conference on Medical and Health Informatics, ICMHI 2018 ; Conference date: 08-06-2018 Through 10-06-2018",

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N2 - Tea originated in Asian, which was initially used as a medicinal herb. The variety of tea is according to different manufacturing processes and levels of oxidation. The different varieties of tea have different level of effects on health, thus this study adopted text mining technique and Latent Dirichlet Allocation (LDA) to analyze literature for tea in health effect. This study chose Web of Science as the database of literature source, and the search literature from 2007 to 2017. The total 1230 journal articles were collected in this study. The title, abstract, and keywords of the collected journal articles were used as a dataset for the experiment. Experimental results show that the VEM method is significantly lower than Gibbs sampling in perplexity. Hence, this study chooses K=150 when VEM method and Gibbs sampling reach the minimal perplexity in the same time. Many topics that related with tea and compounds of tea, however some topics had terms that related to health and disease. The top 10 topics show that tea could reduce the risk of diseases and benefit of health.

AB - Tea originated in Asian, which was initially used as a medicinal herb. The variety of tea is according to different manufacturing processes and levels of oxidation. The different varieties of tea have different level of effects on health, thus this study adopted text mining technique and Latent Dirichlet Allocation (LDA) to analyze literature for tea in health effect. This study chose Web of Science as the database of literature source, and the search literature from 2007 to 2017. The total 1230 journal articles were collected in this study. The title, abstract, and keywords of the collected journal articles were used as a dataset for the experiment. Experimental results show that the VEM method is significantly lower than Gibbs sampling in perplexity. Hence, this study chooses K=150 when VEM method and Gibbs sampling reach the minimal perplexity in the same time. Many topics that related with tea and compounds of tea, however some topics had terms that related to health and disease. The top 10 topics show that tea could reduce the risk of diseases and benefit of health.

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KW - Latent Dirichlet Allocation

KW - LDA

KW - Literature analysis

KW - Tea

KW - Text mining

KW - Topic model

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ER -

Tea in benefits of health: A literature analysis using text mining and latent dirichlet allocation

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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