‘You will like it!’ using open data to predict tourists' response to a tourist attraction

Abstract

The increasing amount of user-generated content spread via social networking services such as reviews, comments, and past experiences, has made a great deal of information available. Tourists can access this information to support their decision making process. This information is freely accessible online and generates so-called “open data”. While many studies have investigated the effect of online reviews on tourists’ decisions, none have directly investigated the extent to which open data analyses might predict tourists’ response to a certain destination. To this end, our study contributes to the process of predicting tourists’ future preferences via MathematicaTM, , software that analyzes a large set of the open data (i.e. tourists reviews) that is freely available on Tripadvisor. This is devised by generating the classification function and the best model for predicting the destination tourists would potentially select. The implications for the tourist industry are discussed in terms of research and practice

Measuring Destination Image through Travel Reviews in Search Engines

“…This selection matches Baka (2016), who considers TripAdvisor the world's largest source of UGC in the domain of tourism, and other authors [11,46], explaining the advantages of collecting a set of open data in TripAdvisor because of the huge amount of user-generated reviews that it hosts. Moreover, Yoo et al [47] note that TripAdvisor's reputation management system helps to determine the helpfulness of reviews and/or reviewers (which enables viewing profiles, other reviews, votes, and ratings) and motivates users to contribute reliable reviews (through intrinsic and extrinsic motivations).…”

An integrative framework for collaborative forecasting in tourism supply chains

“…The use of big data in tourism forecasting has only recently gained academic attention. For example, Pantano et al () investigate the extent to which open data analyses can predict tourists' responses to a certain destination and Song and Liu () propose a framework for tourism forecasting with big data.…”

“…Travellers tend to use online communities, such as TripAdvisor, Facebook, and Twitter, to obtain and share information. For instance, Pantano et al () predict tourists' responses to a tourist attraction using TripAdvisor data.…”

“…Their results suggest that the proposed method improves forecast accuracy more than a traditional time‐series model and a PCA‐based index model. Pantano, Priporas, and Stylos () attempt to predict tourists' process of selecting tourist destinations and/or services by transforming large amounts of open data into value propositions. Travellers generate various information on open sources, such as TripAdvisor and social networks (e.g., Facebook and Twitter).…”

“…Travellers generate various information on open sources, such as TripAdvisor and social networks (e.g., Facebook and Twitter). Such information affects other travellers' decision‐making processes, such as their selection of a tourist destination, accommodations, and attractions to visit (Filieri, Alguezaui, & McLeay, ; Hudson, ; Pantano & Di Pietro, ; Pantano et al, ; Xiang, Magnini, & Fesenmaier, ). Recognizing the benefits and challenges of big data analytics to tourism demand forecasters, Song and Liu () propose a tourism demand forecasting framework using big data.…”