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Ȩ Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö > TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) Scenic Image Research Based on Big Data Analysis - Take China's Four Ancient Cities as an Example
¿µ¹®Á¦¸ñ(English Title) Scenic Image Research Based on Big Data Analysis - Take China's Four Ancient Cities as an Example
ÀúÀÚ(Author) Rui Liang   Hanwen Guo   Jiayu Liu   Ziyang Liu  
¿ø¹®¼ö·Ïó(Citation) VOL 14 NO. 07 PP. 2769 ~ 2784 (2020. 07)
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(Korean Abstract)
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(English Abstract)
This paper aims to compare the scenic images of four ancient Chinese cities including Lijiang, Pingyao, Huizhou and Langzhong, so as to provide specific development strategies for the ancient cities. In this paper, the ancient cities¡¯ scenic images are divided into three sub-indexes and eight evaluation dimensions. Based on this, the study first uses Python software to collect tourists¡¯ online comments on the four ancient cities. Then, the social network analysis method is used to build a high-frequency keywords matrix of tourist comments and the R language is used to generate a visual network graph. After this, the entropy weight method is used to determine the weights and values of eight evaluation dimensions. Finally, the tourists¡¯ overall satisfaction indexes of the four ancient cities are calculated accordingly. The results show that (1) the overall satisfaction of Lijiang is the highest, while that of Huizhou is the lowest; (2) from the weight of each evaluation dimension, it can be seen that tourists care more about the national culture and historical culture; (3) from tourists¡¯ satisfaction index on each evaluation dimension of the four ancient cities, we can find that the four ancient cities has their own advantages and disadvantages in tourism development. (4) local tourism-related institutions should strengthen their advantages and improve their deficiencies so as to enhance tourists¡¯ overall image of the ancient city.
Å°¿öµå(Keyword) Scenic Image   Big Data   Ancient city   Content Analysis Method   Social Network Analysis   Entropy Weight Method.  
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