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Open AccessFeature PaperArticle

From Hotel Reviews to City Similarities: A Unified Latent-Space Model

Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi, 24-10129 Torino, Italy
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 197;
Received: 13 December 2019 / Revised: 12 January 2020 / Accepted: 15 January 2020 / Published: 20 January 2020
(This article belongs to the Special Issue Big Data Analytics for Smart Cities)
A large portion of user-generated content published on the Web consists of opinions and reviews on products, services, and places in textual form. Many travellers and tourists routinely rely on such content to drive their choices, shaping trips and visits to any place on earth, and specifically to select hotels in large cities. In the context of hospitality management, a challenging research problem is to identify effective strategies to explain hotel reviews and ratings and their correlation with the urban context. Under this umbrella, the paper investigates the use of sentence-based embedding models to deeply explore the similarities and dissimilarities between cities in terms of the corresponding hotel reviews and the surrounding points of interests. Reviews and point of interest (POI) descriptions are jointly modelled in a unified latent space, allowing us to deeply investigate the dependencies between guest feedbacks and the hotel neighborhood at different aggregation levels. The experiments performed on public TripAdvisor hotel-review datasets confirm the applicability and effectiveness of the proposed approach. View Full-Text
Keywords: text mining; deep natural language processing; open data; frequent itemset mining text mining; deep natural language processing; open data; frequent itemset mining
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Cagliero, L.; La Quatra, M.; Apiletti, D. From Hotel Reviews to City Similarities: A Unified Latent-Space Model. Electronics 2020, 9, 197.

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