Big data in tourism research: A literature review

Li, Jingjing; Xu, Lizhi; Tang, Ling; Wang, Shouyang; Li, Ling

doi:10.1016/j.tourman.2018.03.009

Cited by 771 publications

(542 citation statements)

References 130 publications

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“…Big data analysis is changing the operating mode of the global tourism economy, providing tourism managers with deeper insights, and infiltrating into all aspects of tourist travels, while driving tourism innovation and development [168]. Tourism text big data mining techniques have made it possible to analyze the behaviors of tourists and realize real-time monitoring of the market.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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“…With fast-growing tourism demands, tourism-related sectors have played a dominant role in the economic systems of most developed and developing countries [1,2]. Global tourist arrivals have increased at an average annual rate of approximately 4% during the past two decades and will grow at a 3.3% annual rate from 2010 to 2030, reaching 1.8 billion visits in 2030 [3].…”

Section: Introductionmentioning

confidence: 99%

Balancing Tourism’s Economic Benefit and CO2 Emissions: An Insight from Input–Output and Tourism Satellite Account Analysis

Tang

et al. 2019

Sustainability

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To balance tourism’s economic benefit and environmental pollution, this paper proposes an analytical approach by using the input–output (IO) model and tourism satellite accounts (TSA). Four steps are taken: (1) the setting of system boundaries according to the combined IO and TSA database; (2) economic benefit estimation for tourism income, sectoral multipliers and inter-sector linkages; (3) environmental pollution estimation of direct and indirect CO2 emissions; and (4) a policy analysis to balance the economic benefit and CO2 emissions (in terms of reducing the CO2 emissions intensity) in tourism-related sectors. In the case of Beijing, some interesting insights can be obtained. Beijing’s tourism sectors experienced a fast economic growth and a clear decrease in CO2 emissions during 2007–2012, with the former having a greater absolute change rate (particularly for the shopping and sightseeing sectors). In all tourism sectors (except for transportation), the indirect CO2 emissions were over three times greater than the direct CO2 emissions. Transportation was a leading contributor to both the economic benefit (representing 91.65% of tourism income in 2012) and to environmental pollution (representing 38.75% of tourism-related CO2 emissions). The detailed findings regarding the industrial and energy structures offer insightful policies for a high-benefit and low-emissions development of tourism.

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“…This approach takes advantage of the booming market of Big Data and the massive adoption of web map servers and browsers, which provide an unprecedented quantity of data for research. Big Data approaches including GPS and geo-tagging in social networks have been widely used in tourism research [70]; however, to our knowledge, web map browsers as tools for geospatial data retrieval have never been used in this context. Map browsers return geolocation results associated with the keywords used for the search, making them an ideal tool to quantitatively measure how keywords are adopted by the "masses".…”

Section: Introductionmentioning

confidence: 99%

Ecotourism in China, Misuse or Genuine Development? An Analysis Based on Map Browser Results

Zhang²,

Gao

et al. 2019

Sustainability

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Ecotourism is considered to be an effective means of promoting nature conservation and sustainable development in less developed regions. However, its widespread adoption may be the result of a misunderstanding due to confusion about definitions and interpretations. Using web map browsers, we assessed the distribution pattern of ecotourism sites in both number and density in the 31 provinces of mainland China, and found that it positively correlated with gross domestic products (GDP) and population size, showing spatial dynamics similar to the general tourism model. However, negative-weak or no correlation at all was found with the presence and size of nature variables such as protected areas. These results support previous suspicions that the term ecotourism and its associated concept may be misused in China and that the regions that could benefit the most from this form of tourism have yet to properly develop it. Although this pattern could reflect a huge demand for genuine ecotourism, we recommend that China, to achieve its ambitious sustainable development goals, adapt ecotourism policies in its environmental and socio-cultural context, manage them with a trans-disciplinary expert board, and regulate its market by introducing a rigorous admittance system with continuous monitoring and evaluation.

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Big data in tourism research: A literature review

Cited by 771 publications

References 130 publications

A Review of Text Corpus-Based Tourism Big Data Mining

A Review of Text Corpus-Based Tourism Big Data Mining

Balancing Tourism’s Economic Benefit and CO2 Emissions: An Insight from Input–Output and Tourism Satellite Account Analysis

Ecotourism in China, Misuse or Genuine Development? An Analysis Based on Map Browser Results

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