Abstract
The relatedness between tourism attractions can be used in a variety of tourism applications, such as destination collaboration, commercial marketing, travel recommendations, and so on. Existing studies have identified the relatedness between attractions through measuring their co-occurrence—these attractions are mentioned in a text at the same time—extracted from online tourism reviews. However, the implicit semantic information in these reviews, which definitely contributes to modelling the relatedness from a more comprehensive perspective, is ignored due to the difficulty of quantifying the importance of different dimensions of information and fusing them. In this study, we considered both the co-occurrence and images of attractions and introduce a heterogeneous information network (HIN) to reorganize the online reviews representing this information, and then used HIN embedding to comprehensively identify the relatedness between attractions. First, an online review-oriented HIN was designed to form the different types of elements in the reviews. Second, a topic model was employed to extract the nodes of the HIN from the review texts. Third, an HIN embedding model was used to capture the semantics in the HIN, which comprehensively represents the attractions with low-dimensional vectors. Finally, the relatedness between attractions was identified by calculating the similarity of their vectors. The method was validated with mass tourism reviews from the popular online platform MaFengWo. It is argued that the proposed HIN effectively expresses the semantics of attraction co-occurrences and attraction images in reviews, and the HIN embedding captures the differences in these semantics, which facilitates the identification of the relatedness between attractions.
Highlights
The relatedness between geographic objects captures a broad relation between objects that can be close or far apart in location, can be linked by interaction, or may share a common property [1]
Haris et al extracted the semantic relationships between tourist places from travel notes through the natural language processing (NLP) technique, constructed a points of interest (POIs) graph to find the popular attractions and popular trip patterns which consist of the related attractions [11]
The emphasis of this research illustrates that the heterogeneous information network (HIN) can retain the difference between different relationship semantics when the online reviews are reorganized into a network structure, and the HIN embedding model can capture and fuse these different relationship semantics, which facilitate identifying the relatedness between attractions from a comprehensive perspective
Summary
The relatedness between geographic objects captures a broad relation between objects that can be close or far apart in location, can be linked by interaction, or may share a common property [1]. Massive travel reviews of tourists are becoming accessible through social networks, such as Yelp, TripAdvisor, Booking, and so on. These reviews support the different types of information about visited attractions, visited times, travel notes and basic profiles of tourists, labels, ranks, review texts, and basic attributes of attractions. The results of identified relatedness are helpful to cognize the tourism movement patterns [6,7], evaluate the market position of different attractions [7,8], and reveal the factors affecting the network structure of the tourist flows [9,10]. Yuan et al implemented the frequent pattern mining method to identify the city’s popular locations by their sequenced co-occurrences from travel blogs, develop a max-confidence-based method to detect travel routes from the popular location network [12]
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