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Research On The Strategy For Improving Management Of 5A-level Tourist Attractions In Shanxi Province Based On Internet Word-of-Mouth

Posted on:2024-04-18Degree:MasterType:Thesis
Country:ChinaCandidate:Y Z ZhengFull Text:PDF
GTID:2568307052491674Subject:Library and Information Science
Abstract/Summary:PDF Full Text Request
With the improvement of communication infrastructure construction and the deepening of the digital transformation of the tourism industry,it has become common for tourists to obtain travel information through the Internet.Among all kinds of online tourism information,Internet word-of-mouth has a particularly important impact on tourists’ decision-making.As the most important tourist destination in the province,the5A-level tourist attractions in Shanxi Province have a close relationship with their online reputation and income.Therefore,it is a big work for the continuous development of tourism in Shanxi Province to dig deeply into the themes and emotional attitudes of people,and to give countermeasures for business improvement.This paper conducts a natural language analysis on the Internet word-of-mouth of5A-level tourist attractions in Shanxi Province,and uses online text analysis,social network analysis and other technical methods to conduct research with tourists’ online comments,China Cultural Tourism Statistical Yearbook,and official websites of scenic spots as research materials.In the data collection stage,the tourist reviews of 5A-level tourist attractions in Shanxi Province on the web page(Ctrip)were used as the data source,and the review data from 2015 to 2022 were crawled,and pre-processing such as Chinese word segmentation was performed.Afterwards,according to the characteristics of highfrequency keywords in the online comments of scenic spots,the emotional dictionary,negative vocabulary and degree table suitable for 5A-level scenic spots in Shanxi Province were expanded,and the emotional value of electronic word-of-mouth and the proportion of different emotional tendencies in each spot.In the topic analysis part,the co-word matrix with PPMI(Positive Point Mutual Information)as the weight is used,and Gephi software is imported for overall network analysis and modular analysis,and the positive and negative online word-of-mouth topics and corresponding keywords of the spots are obtained.According to the key points of tourists’ attention under different emotional tendencies,the advantages and disadvantages of tourist attractions in Shanxi Province are discovered from the perspective of tourists,and countermeasures are provided for the improvement of scenic spot management.The research shows that 81.27%of the review texts have a positive emotional tendency,and tourists generally have a subjectively positive attitude towards their travel experience in 5A-level scenic spots in Shanxi Province.Among them,Yungang Grottoes in Datong has the highest emotional value of Internet word-of-mouth,and the highest proportion of positive word-of-mouth.Yunqiu Mountain Scenic Area in Linfen,Mianshan Mountain Scenic Area in Jiexiu and Baquan Gorge,Taihang Mountain Grand Canyon accounted for a relatively high proportion of negative word-of-mouth.The modularity coefficients of positive emotional IWOM in each scenic spot are lower than those of negative emotional IWOM keywords,and the proportion of adjectives in negative emotional IWOM keywords is higher,and the theme analysis effect is more significant.It can be inferred that this paper gives some measures to promote the management of 5A-level scenic spots in Shanxi Province from four aspects: pay attention to the construction of Internet word-of-mouth and maintain a good word-of-mouth advantage;reasonably adjust the consumption price of scenic spots,strengthen real-time management;combine scenic spots Develop characteristic cultural and entertainment projects based on historical and cultural background;strengthen cooperation with local governments,update and improve infrastructure.
Keywords/Search Tags:Internet word of mouth, Shanxi Province, scenic spots, sentiment analysis, social network analysis
PDF Full Text Request
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