A study on online travel reviews through intelligent data analysis

Fazzolari, Michela; Petrocchi, Marinella

doi:10.1007/s40558-018-0121-z

Cited by 21 publications

(12 citation statements)

References 35 publications

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“…The OTRs contain enough information to identify the resource and place it in time and space. There may be inconsistency with the time dimension due to lag between the visit and publication dates of the OTR [ 100 , 101 ]; that is, between the perceived and projected images. This study only considers the publication date because the OTR can be consulted by any user at any time.…”

Section: Methodsmentioning

confidence: 99%

Gastronomy Tourism and Well-Being: Evidence from Taiwan and Catalonia Michelin-Starred Restaurants

Lin

Mariné-Roig

Molina

2022

IJERPH

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In the tourism and hospitality industry, ensuring the well-being of visitors is essential to achieving a competitive tourist destination. This objective is even more pressing in the gastronomy sector. Surprisingly, the scientific literature on this topic is scarce and relies on questionnaire surveys and interviews as a data source. After scrutinizing the 13 articles on gastronomy tourism and well-being indexed in the Web of Science or in Scopus, this study proposes two new lines of research interrelated by the concept of gastronomic image. These exploit the content shared online by consumers in order to assess subjective well-being derived from quality gastronomic experiences. The first is a framework for the customer-perceived image based on Grönroos’s service quality model, and the second is a conceptual model based on Morris’s semiotics to measure gastronomic image. Through mixed methodologies, i.e., qualitative in the first research line and quantitative in the second, the study applies the theoretical framework to Michelin-starred restaurants in two tourist regions with similar features but with different gastronomic cultures—Taiwan (Asia) and Catalonia (Europe)—using as a data source all the online travel reviews (OTRs) written in English about these restaurants shared on the TripAdvisor portal. Comparing the three categories of restaurants in both regions, the results show branding and marketing problems and significant differences in the popularity of restaurants and the satisfaction and well-being of diners. There is a positive relationship between the category of restaurants according to the number of Michelin stars and their popularity according to the number of OTRs, as well as with the satisfaction and well-being of diners, except for a 3-star restaurant that is the worst-rated. These outcomes from the demand side can be useful to stakeholders to design or improve gastronomic products and services.

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Section: Methodsmentioning

confidence: 99%

Gastronomy Tourism and Well-Being: Evidence from Taiwan and Catalonia Michelin-Starred Restaurants

Lin

Mariné-Roig

Molina

2022

IJERPH

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“…In services management literature, text mining has been extensively used for assessing different parameters of services such as quality, engagement, and impact (Kumar et al, 2021 ). Sentiment analysis was done on the tweets to measure the polarity in discussions among tourists across India using natural language processing (Fazzolari & Petrocchi, 2018 ) and a semantic approach (Chang & Chen, 2019 ; Kar, 2020 ). Here, “syuzhet” package of R language is used for the purpose of this objective.…”

Section: Methodsmentioning

confidence: 99%

Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach

Kar

Kumar

Ilavarasan

2021

Glob J Flex Syst Manag

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Among services, the immense growth of Indian tourism in the last years has attracted the interest of practitioners, researchers, and governments. Service experiences at the point of encounter can impact the consumption of these tourism services extensively. However, measuring the service experience at the point of service encounter becomes a bit difficult. The tourists who visit India often share their experiences immediately regarding their service encounter in social media. These tweets often have high sentiments and emotional content. In this study, we attempt to identify factors which impact customer service experience, at the point of service encounter, by mining social media discussions. After removing spurious tweets, 7,91,804 tweets were identified and analysed in this study. Factors such as accessibility, accommodation, assurance, cultural attraction, Jugaadu service flexibility, cleanliness, hospitality, price, restaurant, and security were identified using topic modelling, topic association mining, and sentiment analysis. We attempt to model these experiences and their drivers across five zones of India, namely North, South, East, West, and North-East India. Our inferential analysis highlights that the importance and impact of these factors differ significantly zone wise across India, which indicates high location specificity of factors which impact the customer service experience. The study elaborates implications for theory and practice based on our findings.

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“…Also, tourists share their travel experiences through important online platforms in the industry such as TripAdvisor (Guo et al, 2017;Vu, Li, Law, & Zhang, 2019). Online travel opinions usually contains opinions of hotels, res-taurants and attractions (Fazzolari & Petrocchi, 2018) that are fundamental elements that customers consult before planning their trips and booking tourist destinations (Xiang, Schwartz, Gerdes, & Uysal, 2014). These opinions are published by consumers who have bought and used a product or service, including the consumer's own experiences during consumption, as well as evaluations of the product used.…”

Section: Introductionmentioning

confidence: 99%

Exploring consumer behavior through user-generated content on TripAdvisor. The case of Holguin destination

Ricardo¹,

Sánchez²,

Labrada³

2022

Rev. interam. ambient. tur.

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The present study explores consumer behavior of tourists through user-generated content by presenting a method that utilizes online reviews and text processing techniques in analyzing behavior patterns. The current study examined 7,856 travel reviews from TripAdvisor written by international tourists visiting Holguin (Cuba) destination. This method includes the overall process of converting raw data into useful information, which comprises selection, pre-processing, data mining and interpretation. Data are collected from online review platforms rather than from traditional databases, and the first step is data crawling. Then, unstructured review content is transformed into suitable formats for analysis. The final step is exploratory data analysis, which includes both data mining and interpretation. Findings show that the cognitive dimension of the experience predominates in consumer's behavior. The study identifies topics that could be used by destination management organizations to promote this destination and highlights the advantages of applying a data science approach.

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A study on online travel reviews through intelligent data analysis

Cited by 21 publications

References 35 publications

Gastronomy Tourism and Well-Being: Evidence from Taiwan and Catalonia Michelin-Starred Restaurants

Gastronomy Tourism and Well-Being: Evidence from Taiwan and Catalonia Michelin-Starred Restaurants

Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach

Exploring consumer behavior through user-generated content on TripAdvisor. The case of Holguin destination

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