Investigating the tourism image of mountain scenic spots in China through the lens of tourist perception

A favorable tourism image of high-quality mountain scenic spots(HQMSS)is crucial for tourism prosperity and sustainability.This paper establishes a framework for investigating the tourism image based on cognitive-emotion theory and uses natural language processing(NLP)tools to clarify the cognition,emotion,and overall tourist image of the HQMSS in China from the perspective of tourist perception.This paper examines the multi-dimensional spatial differentiation of China's overall image,including province,scenic spot scales,as well as the spatial pattern of the overall comprehensive tourism image.Strategies for comprehensively improving HQMSS's tourism image are also formulated.The results show that:(1)The cognitive image of Chinese HQMSS is categorized into core and marginal images,and the core images such as scenery and cable car are the expression of the uniqueness of mountainous scenic spots.Additionally,the cognitive image is classified into six dimensions:tourism environment,tourism supporting facilities,tourism experience,tourism price,tourism service,and tourism safety.(2)Positive emotions are the dominant mood type of HQMSS in China,followed by neutral emotions,with negative emotions being the least frequent.Emotional images vary across dimensions,with tourism environment and tourism experience evoking relatively higher emotion.(3)The spatial pattern of HQMSS for each dimension at the national,provincial,and scenic scales is diversifying.This article provides a multidimensional perspective for investigating the tourism image of mountainous scenic spots,proposes targeted recommendations to improve the overall image of HQMSS in China,and can greatly contribute to the sustainable development of mountain tourism.

Tourism Image Perception Research of Mount Tai Scenic Area: Text Analysis Based on Ctrip Tourist Comments

Tourism destination image research has always been a hot issue in tourism research, which is related to the development and sustainability of destination tourism. Mount Tai Scenic Area, a world double-heritage site is taken as the research object. By collecting comments about Mount Tai Scenic Area on Ctrip, and using content analysis, cognitive image, emotional image and overall image of Mount Tai Scenic Area are analyzed with the help of “cognitive-emotional” model. The results show that visitors' perception of the cognitive image of Mount Tai Scenic Area is multi-dimensional;tourists' perception of the emotional image of Mount Tai Scenic Area is positive;the tourists' perception of the overall image of Mount Tai Scenic Area shows a four-layer “core-edge” structure of “core-sub core-sub edge-edge”. On this basis, the paper puts forward some suggestions for improving the tourism development of Mount Tai Scenic Area.

MDIVis: Visual analytics of multiple destination images on tourism user generated content

Abundant tourism user-generated content(UGC)contains a wealth of cognitive and emotional in-formation,providing valuable data for building destination images that depict tourists’experiences and appraisal of the destinations during the tours.In particular,multiple destination images can assist tourism managers in exploring the commonalities and differences to investigate the elements of interest of tourists and improve the competitiveness of the destinations.However,existing methods usually focus on the image of a single destination,and they are not adequate to analyze and visualize UGC to extract valuable information and knowledge.Therefore,we discuss requirements with tourism experts and present MDIVis,a multi-level interactive visual analytics system that allows analysts to comprehend and analyze the cognitive themes and emotional experiences of multiple destination images for comparison.Specifically,we design a novel sentiment matrix view to summarize multiple destination images and improve two classic views to analyze the time-series pattern and compare the detailed information of images.Finally,we demonstrate the utility of MDIVis through three case studies with domain experts on real-world data,and the usability and effectiveness are confirmed through expert interviews.