The evolution of consumers’ demand for hotels under the public health crisis: opinion mining from online reviews

Previous research indicates that personalized tourism recommendation (PTR) is becoming increasingly important in tourism marketing. However, many areas of PTR remain unexplored. This study is based on Stimulus-Organism-Response theory; integrated constructs from PTR, big data, and artificial intelligence; and the technology acceptance model. The quantitative approach was conducted through an online survey from 496 users of Ctrip. PLS-SEM was used to test the collected data. Three factors were found to stimulate consumers’ perceptions of PTR: perceived personalization, visual appearance, and information quality. Consumers’ reactions to PTR can be divided into an internal processing organism, which includes the perception of the technology as “technology trust” and the perception of the recommended content as “PTR attitude.” This study contributes to the literature on smart tourism and marketing by developing and empirically testing an integrated model and providing a guide to determine users’ trust and attitudes toward PTR or other personalized e-services.

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“…Ctrip is commonly employed in tourist behavior research (Lan et al, 2021; X. Li et al, 2022; W. Yu et al, 2023; Q.…”

Section: Methodsmentioning

confidence: 99%

Personalized Tourism Recommendations and the E-Tourism User Experience

Yang

Zhang

Zhou

2023

Journal of Travel Research

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“…The complexity of text data is leading people to shift from traditional manual visual screening to computer-based algorithmic model construction and intelligent evaluation, aiming to maximize the efficiency and reliability of data processing workflows [18][19][20]. Online hotel booking platforms contain vast amounts of reviews from tourists [21], including information about sentiments [22], demands [23], and improvement suggestions [24]. If improvement suggestions and demands from reviews could be accurately extracted, tourist hotels could optimize their services and enhance tourist satisfaction based on tourist feedback and demands.…”

Section: Introductionmentioning

confidence: 99%

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

Wang,

Zhu,

Zhang

et al. 2024

Smart. Tour.

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<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>

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A Real-Time Impact Study of New Product Release on Theme Park Brand Reputation: Using Social Media Data from Shanghai Disneyland

Deng,

Liu

2024

Lecture Notes on Data Engineering and Communications Technologies

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The evolution of consumers’ demand for hotels under the public health crisis: opinion mining from online reviews

Cited by 8 publications

References 58 publications

Personalized Tourism Recommendations and the E-Tourism User Experience

Personalized Tourism Recommendations and the E-Tourism User Experience

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

A Real-Time Impact Study of New Product Release on Theme Park Brand Reputation: Using Social Media Data from Shanghai Disneyland

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