[논문]Research on Satisfaction Evaluation Based on Tourist Big Data

Guo, Hanwen; Liu, Ziyang; Jiao, Zeyu

doi:10.3837/tiis.2022.01.013

Research on Satisfaction Evaluation Based on Tourist Big Data 원문보기

KSII Transactions on internet and information systems : TIIS, v.16 no.1, 2022년, pp.231 - 244

Guo, Hanwen (Business School, GuangXi University for Nationalities) , Liu, Ziyang (Department of Global business, University of Kyonggi Suwon) , Jiao, Zeyu (Department of Global business, University of Kyonggi Suwon)

Abstract ▼ AI-Helper

With the improvement of people's living standards and the development of tourism, tourists have greater freedom in choosing destinations. Therefore, as an indicator of satisfaction with scenic spots, tourist comments are becoming increasingly prominent. This paper aims to compare and analyze the landscape image of the Five Great Mountains in China and provide specific strategies for its development. The online reviews of tourists on the Online Travel Agency (OTA) website about the Five Great Mountains from 2015 to 2018 are collected as research samples. The text analysis method and R language are used to analyze the content of the tourist reviews, while the high-frequency words in the word cloud are used for visual display. In addition, the entropy weight method is used to determine the index weight and tourist satisfaction is evaluated to understand the weaknesses of those scenic spots. The results of the study show that firstly, the tourist satisfaction with the Five Great Mountains is basically consistent with its popularity. Secondly, through weight analysis, tourists pay special attention to the landscape features and environmental health of the scenic area, so that relevant departments should focus on building the landscape characteristics and improving the environmental health of the scenic area. At the same time, the accommodation and service management of the scenic spot cannot be ignored. Finally, according to the analysis results, suggestions are made on how to improve the tourist satisfaction with the Five Great Mountains.

주제어

표/그림 (4)

그림 Fig. 1. High-frequency word cloud of the Five Great Mountains
표 Table 1. High-frequency characteristic words and word frequency statistics of the Five Great Mountains
표 Table 2. Evaluation Index System of Tourist Satisfaction in the Five Great Mountains
표 Table 3. Tourist Satisfaction Ranking and Calculation Results of the Five Great Mountains

AI 본문요약
AI-Helper

제안 방법

This paper has made some contributions in related research fields. As for the research objects, this study uses the method of quantifying the satisfaction of scenic spots to compare and study the satisfaction of tourists in the Five Great Mountains, which have high tourist reviews on the OTA website. It is of certain significance for the tourism industry of China to achieve differentiated development through mutual comparison and reference.

이론/모형

The sentiment classification is divided into 3 comments, which are positive comments, neutral comments and negative comments. In this paper, the fuzzy comprehensive evaluation method and the normalization method are used to process the sentences of the sentiment classification. The weights of the three reviews are 1, 0.

성능/효과

However, there are still some limitations in this study. Firstly, in terms of data samples, the data collected from three travel platforms needs to be further expanded. Since the user information of tourists cannot be identified, it is difficult to determine the representativeness of the samples, so that it is impossible to determine the satisfaction of tourists from a scientific perspective.
Secondly, the paper does not categorize the content of the reviews in time, but only calculates the content and semantics according to the weight score during the calculation. For example, some tourists made suggestions in 2015 and the scenic spot management department may have rectified the problem in 2016, so that some scenic spot projects cannot obtain an absolute objective score when doing the calculation, which affects the tourist satisfaction ranking of the scenic spot. Finally, with regard to research methods, this paper mainly uses text analysis, followed by semantic classification and sentiment analysis to screen the text.
A multi-dimensional quantitative comparative analysis of satisfaction is of certain significance for discovering the advantages and disadvantages of different tourist attractions to achieve precise development. In addition, in the construction of evaluation indicators for scenic spots, this paper combines existing research results and the word frequency characteristics of the evaluation of the Five Great Mountains, dividing the scenic spot into 9 sub-evaluation dimensions, which enrich the related research on scenic spot evaluation to a certain extent.
Since the user information of tourists cannot be identified, it is difficult to determine the representativeness of the samples, so that it is impossible to determine the satisfaction of tourists from a scientific perspective. Secondly, the paper does not categorize the content of the reviews in time, but only calculates the content and semantics according to the weight score during the calculation. For example, some tourists made suggestions in 2015 and the scenic spot management department may have rectified the problem in 2016, so that some scenic spot projects cannot obtain an absolute objective score when doing the calculation, which affects the tourist satisfaction ranking of the scenic spot.

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