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With the considerable development of tourism market, as well as the expansion of the e-commerce platform scale, increasing tourists often prefer to select tourism products such as services or hotels online. Thus, it needs to provide an efficient decision support model for tourists to select tourism products. Online reviews based on the user experience would help tourists improve decision efficiency on tourism products. Therefore, in this study, a quantitative method for hotel selection with online reviews is proposed. First, with respect this problem with online reviews, by analyzing sentiment words in online reviews, tourists’ sentiment preferences are transformed into the format of distribution linguistic with respect to sentiment levels. Second, from a theoretical perspective, we proposed a method to determine the ideal solution and nadir solution for distribution linguistic evaluations. Next, based on the frequency of words for evaluating hotel and the distribution linguistic evaluations, the weight vector of the evaluation features is determined. Further, a novel DL-VIKOR method is developed to rank and then to select hotels. Finally, a realistic case from TripAdvisor.com for selecting hotel is used to demonstrate practically and feasibility of the proposed model.
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Stored only when both papers are in the corpus — 74 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.