Listen to the voices from home: An analysis of Chinese tourists’ sentiments regarding Australian destinations

Liu, Yi; Huang, Kaixuan; Bao, Jigang; Chen, Kaiqi

doi:10.1016/j.tourman.2018.10.004

Cited by 133 publications

(108 citation statements)

References 38 publications

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“…By the analysis of preferences and behaviors, tourists can be divided into more groups such as travel buyers, the decompressions, and so on. Different groups have different characteristics, and individuals with the same attributes may have similarities in tourism behavior [143]. Study [73] has confirmed that differences in tourist attributes can also lead to differences in tourist perceptions.…”

Section: Tourist Profilementioning

confidence: 82%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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Section: Tourist Profilementioning

confidence: 82%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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“…The market potential of inbound tourist flow mainly depends on the attributes of the tourism supply side, such as the industrial structure, resource endowment, climate comfort index, destination image [38], price level, and traffic conditions of the destination [43,49]. More importantly, due to the differences in culture, endowment, geography, economy, policies, and other aspects among different destinations (province or autonomous region) [53][54][55], the tourism demand elasticity intensity of different travel motivation segments for various variables is significantly different, which is consistent with our theoretical expectations. This difference was the starting point and foothold of this study and the basis of the proposed development path.…”

Section: Discussionmentioning

confidence: 99%

“…in the inbound tourism market (foreigners, Hongkonger tourists, Macanese tourists, and Taiwanese tourists) of China. We combined elasticity differences of various motivations, which are caused by different variables such as traffic conditions [51,52], consumer price index (CPI) [53], resource endowments, destination image [54,55], climate environment, industrial structure, and infrastructure [56] to explore the market potential development model and path that suits the unbalanced distribution of inbound tourist flow in provinces and autonomous regions. Finally, we propose countermeasures and suggestions that are more practical.…”

Section: Literature Reviewmentioning

confidence: 99%

The Spatial Distribution, Influencing Factors, and Development Path of Inbound Tourism in China—An Empirical Analysis of Market Segments Based on Travel Motivation

et al. 2020

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According to China’s tourism statistics, the inbound tourism market is composed of eight types of travel motivations (sightseeing, leisure, business meeting (business-m), visiting relatives and friends (visiting-rf), shopping, religious worship (religious-w), culture and sports (culture-s), and health care (health-c)), and the spatial distribution of each type of travel motivation is significantly different. Four inbound sub-markets (foreigners, Hong Kong, Macao, and Taiwan) were selected as our research object. Through empirical analysis of the variable elasticity of eight different inbound motive market segments, we found that the sensitivities (elasticity) of the influencing factors (traffic conditions (traffic-c), destination image (destination-i), industry structure (industry-s), infrastructure, consumer price index (CPI), resource endowment (resource-e), and dressing index (ICL)) are different. Therefore, investment options in the target market can have differential treatment based on the rate of marginal return on investment. In accordance with the characteristics of different market segments, we suggest more feasible development paths and countermeasures, providing a decision-making basis for the accurate development of the inbound tourism market.

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“…(i.e. Rahmani et al, 2018;Liu et al, 2019;Yan et al, 2018;Gräbner et al, 2012) or determining their attitudes by either using questionnaires or being interviewed (Ivanović and Milojica, 2018;Hosany, 2012;Gnoth et al 2000;Servidio and Rufolo, 2016;Hosany et al, 2014;Hosany and Gilbert, 2009) etc. In order to get a clearer insight the authors will present some of them.…”

Section: The Importance Of Determining Tourist´s Sentimentsmentioning

confidence: 99%

Measuring Satisfaction and Experienced Sentiments of Website Users When Exploring Croatian Gastronomic Tourist Offer

Ivanović¹,

Klimoska²,

Milojica³

2019

Tourism in Southern and Eastern Europe

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Purposethis paper presents the importance and necessity of measuring satisfaction of website users with the texts presenting the Croatian gastronomic offer, and their experienced sentiments during and after browsing. The goal is to determine the influence of these texts on successful decision-making process when choosing Croatia as a gastronomic holiday destination. Methodologythe empirical research was done in February 2019 using a structured questionnaire which was distributed online. The acquired data was analyzed using the SPSS 21 Statistical package. The authors used Cronbach Alpha Coefficient to test the questionnaire validity, as well as Pearson´s Coefficient of Correlation, and T-Test and Levene´s Test for Equality for variances. Findings -By using Pearson´s Coefficient of Correlation a positive correlation had been determined indicating that the growth of satisfaction of website users with the texts will result in experiencing positive sentiments towards Croatia as destination. No that no statistically significant difference has been determined for satisfaction in relation to their monthly income. By using Ttest and Levene´s Test for Equality of Variances a statistically significant difference for sentiments has been determing in relation to the respondent´s gender. A a statistically significant difference has been determined for positive sentiments in relation to the respondent´s gender, which was not the case for satisfaction and gender. Contribution -The results enable better understanding of where to focus in text improvements (its quality, diversity, etc.). The main goal has to be better success in tourist offer promotion, and positive decision when choosing Croatia as a holiday destination.

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Listen to the voices from home: An analysis of Chinese tourists’ sentiments regarding Australian destinations

Cited by 133 publications

References 38 publications

A Review of Text Corpus-Based Tourism Big Data Mining

A Review of Text Corpus-Based Tourism Big Data Mining

The Spatial Distribution, Influencing Factors, and Development Path of Inbound Tourism in China—An Empirical Analysis of Market Segments Based on Travel Motivation

Measuring Satisfaction and Experienced Sentiments of Website Users When Exploring Croatian Gastronomic Tourist Offer

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