Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan

Jui-Hung Chang, Chien Yuan Tseng, Ren Hung Hwang, Mi-Chia Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Google's search engine has recorded the popularity of a great number of tourism-related hot words. Prior to vacationing, many people will search the four dimensions of tourism, namely food, fashion, accommodation and transportation, on the Internet before an overseas trip. Exploring the correlation between popularity trends of tourism-related hot words and the number of tourists visiting a particular destination is a potentially valuable research area for the tourist industry. Therefore, this study counted the occurrence frequency of words related to Japanese tourism in the Google search engine and in tourism articles on electronic news websites. With these data, it calculated the Pearson correlation coefficient of the number of Taiwanese tourists visiting Japan "n" months later. Additionally, a deep learning (Artificial Neural Network) model was established, and the relationship between the popularity scores of tourism-related hot words and the interval of the number of Taiwanese tourists in Japan was examined. The research results show that the popularity of tourism-related hot words on Google is highly related to the number of Taiwanese tourists visiting Japan.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages132-137
Number of pages6
Volume2018-January
ISBN (Electronic)9780769563282
DOIs
Publication statusPublished - 2018 Mar 13
Event7th IEEE International Symposium on Cloud and Service Computing, SC2 2017 - Kanazawa, Japan
Duration: 2017 Nov 222017 Nov 25

Other

Other7th IEEE International Symposium on Cloud and Service Computing, SC2 2017
CountryJapan
CityKanazawa
Period17-11-2217-11-25

Fingerprint

Search engines
Websites
Internet
Neural networks
Industry
Japan
Tourists
Tourism
Deep learning
Google

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Information Systems and Management

Cite this

Chang, J-H., Tseng, C. Y., Hwang, R. H., & Ma, M-C. (2018). Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan. In Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017 (Vol. 2018-January, pp. 132-137). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SC2.2017.27
Chang, Jui-Hung ; Tseng, Chien Yuan ; Hwang, Ren Hung ; Ma, Mi-Chia. / Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers : A Case Study in Japan. Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 132-137
@inproceedings{d82dbd5d8ad34acd841590128ccb478e,
title = "Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan",
abstract = "Google's search engine has recorded the popularity of a great number of tourism-related hot words. Prior to vacationing, many people will search the four dimensions of tourism, namely food, fashion, accommodation and transportation, on the Internet before an overseas trip. Exploring the correlation between popularity trends of tourism-related hot words and the number of tourists visiting a particular destination is a potentially valuable research area for the tourist industry. Therefore, this study counted the occurrence frequency of words related to Japanese tourism in the Google search engine and in tourism articles on electronic news websites. With these data, it calculated the Pearson correlation coefficient of the number of Taiwanese tourists visiting Japan {"}n{"} months later. Additionally, a deep learning (Artificial Neural Network) model was established, and the relationship between the popularity scores of tourism-related hot words and the interval of the number of Taiwanese tourists in Japan was examined. The research results show that the popularity of tourism-related hot words on Google is highly related to the number of Taiwanese tourists visiting Japan.",
author = "Jui-Hung Chang and Tseng, {Chien Yuan} and Hwang, {Ren Hung} and Mi-Chia Ma",
year = "2018",
month = "3",
day = "13",
doi = "10.1109/SC2.2017.27",
language = "English",
volume = "2018-January",
pages = "132--137",
booktitle = "Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Chang, J-H, Tseng, CY, Hwang, RH & Ma, M-C 2018, Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan. in Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 132-137, 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017, Kanazawa, Japan, 17-11-22. https://doi.org/10.1109/SC2.2017.27

Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers : A Case Study in Japan. / Chang, Jui-Hung; Tseng, Chien Yuan; Hwang, Ren Hung; Ma, Mi-Chia.

Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 132-137.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers

T2 - A Case Study in Japan

AU - Chang, Jui-Hung

AU - Tseng, Chien Yuan

AU - Hwang, Ren Hung

AU - Ma, Mi-Chia

PY - 2018/3/13

Y1 - 2018/3/13

N2 - Google's search engine has recorded the popularity of a great number of tourism-related hot words. Prior to vacationing, many people will search the four dimensions of tourism, namely food, fashion, accommodation and transportation, on the Internet before an overseas trip. Exploring the correlation between popularity trends of tourism-related hot words and the number of tourists visiting a particular destination is a potentially valuable research area for the tourist industry. Therefore, this study counted the occurrence frequency of words related to Japanese tourism in the Google search engine and in tourism articles on electronic news websites. With these data, it calculated the Pearson correlation coefficient of the number of Taiwanese tourists visiting Japan "n" months later. Additionally, a deep learning (Artificial Neural Network) model was established, and the relationship between the popularity scores of tourism-related hot words and the interval of the number of Taiwanese tourists in Japan was examined. The research results show that the popularity of tourism-related hot words on Google is highly related to the number of Taiwanese tourists visiting Japan.

AB - Google's search engine has recorded the popularity of a great number of tourism-related hot words. Prior to vacationing, many people will search the four dimensions of tourism, namely food, fashion, accommodation and transportation, on the Internet before an overseas trip. Exploring the correlation between popularity trends of tourism-related hot words and the number of tourists visiting a particular destination is a potentially valuable research area for the tourist industry. Therefore, this study counted the occurrence frequency of words related to Japanese tourism in the Google search engine and in tourism articles on electronic news websites. With these data, it calculated the Pearson correlation coefficient of the number of Taiwanese tourists visiting Japan "n" months later. Additionally, a deep learning (Artificial Neural Network) model was established, and the relationship between the popularity scores of tourism-related hot words and the interval of the number of Taiwanese tourists in Japan was examined. The research results show that the popularity of tourism-related hot words on Google is highly related to the number of Taiwanese tourists visiting Japan.

UR - http://www.scopus.com/inward/record.url?scp=85050796354&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050796354&partnerID=8YFLogxK

U2 - 10.1109/SC2.2017.27

DO - 10.1109/SC2.2017.27

M3 - Conference contribution

AN - SCOPUS:85050796354

VL - 2018-January

SP - 132

EP - 137

BT - Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Chang J-H, Tseng CY, Hwang RH, Ma M-C. Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan. In Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 132-137 https://doi.org/10.1109/SC2.2017.27