Abstract
This article taking the travel notes of the ant mafengwo.com and Ctrip.com as a sample, using the content analysis method and ROST CM6 to analyze the visitors’ perception of the Guilin tourism destination image, through the analysis of the high-frequency vocabulary and the semantics of the network notes, and the spindle coding, From eight categories of humanistic attraction, natural attraction, tourism transportation, special food, accommodation conditions, overall impression, tourism consumption and service level, It is found that the tourists’ perception of Guilin tends to be positive. The basic information characteristics, cognitive image, emotional image and willingness to travel are comprehensively explored. The image of Guilin is proposed from the improvement of hardware elements, the innovation of tourism image marketing methods and the improvement of software image elements.
Highlights
This article uses the "Travel Notes" of ant mafengwo.com and Ctrip.com as the data source
The selected 200 online travel notes were coded in an open way, and word frequency analysis was conducted with the help of ROST CM6 as an open coding tool
The comprehensive perception of tourists' image of Guilin's tourist destination is summarized as follows: Tourists are generally satisfied with the natural attraction of the mountains, the beauty, the paradise of the world, and the human attraction, which is represented by the Lijiang River, Yangshuo, Elephant Trunk Hill Park and Gudong Forest Park
Summary
This article uses the "Travel Notes" of ant mafengwo.com and Ctrip.com as the data source. The selection conditions are as follows: First, the time span is from January 1, 2018 to December 31, 2018; s Secondly, travel notes with more than 500 hits and more than 10 replies; Thirdly, Guilin is the main tourist route. A total of 200 eligible travel notes and 1 million words were selected after screening. This article use content analysis, which converts text on the media, valuabl non-quantitative information into quantifiable data, and establishes meaningful category decomposition information [2]. The selected 200 online travel notes were coded in an open way, and word frequency analysis was conducted with the help of ROST CM6 as an open coding tool. The more words appear, the higher the tourists' perception and consensus, which is the core of cognitive image. The concept is coded in principal axis
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