TY - GEN
T1 - Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers
T2 - 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017
AU - Chang, Jui Hung
AU - Tseng, Chien Yuan
AU - Hwang, Ren Hung
AU - Ma, Mi Chia
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
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
T3 - Proceedings - 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017
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.
Y2 - 22 November 2017 through 25 November 2017
ER -