Utilizing big data in tourism marketing

Ye, Xin; Liu, Wen; Li, Longgang; Zhang, Yaning

doi:10.2991/isss-17.2017.54

Cited by 2 publications

(2 citation statements)

References 5 publications

(6 reference statements)

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“…As an industry highly dependent on information data, tourism is one of the industries with the broadest application prospects of big data, which mainly includes UGC data, equipment data and transaction data [1] , among which UGC data, which usually explicitly or implicitly records the habits and preferences of tourists, has become one of the main forms of tourism big data [2] . How to dig out the potential value of tourism UGC data has become a hot topic in current research.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Tourism Review Information Based on Data Mining Technology

2023

Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, N

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In recent years, with the rapid development of data mining technology, it has gradually begun to analyze and apply complex data in various fields. Tourism industry is one of the industries with the broadest application prospect of data mining technology. It can excavate and analyze tourist preference and provide marketing decision basis for tourism management department. This paper takes the online tourism review data of 5A scenic spots in Zhejiang Province as the research object to conduct in-depth text mining and analysis. First, the python program is used to capture relevant online review texts on the Ctrip, and the tourism review data set is constructed. By extracting high-frequency words, the preliminary mining and analysis are carried out. Secondly, the supervised learning algorithm based on machine learning is used for further emotion classification and quantification. Then, the LDA topic model and visualization method are used to extract the subject words of the text data, and the subject classification is carried out. Finally, reasonable suggestions are put forward according to the experimental results.

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Section: Introductionmentioning

confidence: 99%

Analysis of Tourism Review Information Based on Data Mining Technology

2023

Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, N

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“…Travelers can access search engines via either personal computers (PCs) or internetenabled mobile devices. Whereas mobile devices allow travelers to perform internet searches wherever and whenever a mobile signal is available (Ye et al, 2017), online searches via PCs are only possible in certain places. This perceived convenience from mobile devices enables users to search for relevant information and services without much effort (Chen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?

Xiao

2021

IJCHM

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Purpose While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting. Design/methodology/approach Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting. Findings Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal. Practical implications Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management. Originality/value This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

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Utilizing big data in tourism marketing

Cited by 2 publications

References 5 publications

Analysis of Tourism Review Information Based on Data Mining Technology

Analysis of Tourism Review Information Based on Data Mining Technology

Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?

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