In this paper, we analyse a dataset of hotel reviews. In details, we enrich the review dataset, by extracting additional features, consisting of information on the reviewers' profiles and the reviewed hotels. We argue that the enriched data can gain insights on the factors that most influence consumers when composing reviews (e.g., if the appreciation for a certain kind of hotel is tied to specific users' profiles). Thus, we apply statistical analyses to reveal if there are specific characteristics of reviewers (almost) always related to specific characteristics of hotels. Our experiments are carried out on a very large dataset, consisting of around 190k hotel reviews, collected from the Tripadvisor website.
Mining implicit data association from Tripadvisor hotel reviews / Cozza, V.; Petrocchi, M.; Spognardi, A.. - 2083:(2018), pp. 56-61. (Intervento presentato al convegno 2018 Workshops of the International Conference on Extending Database Technology and the International Conference on Database Theory, EDBT/ICDT-WS 2018 tenutosi a Vienna).
Mining implicit data association from Tripadvisor hotel reviews
Spognardi A.
2018
Abstract
In this paper, we analyse a dataset of hotel reviews. In details, we enrich the review dataset, by extracting additional features, consisting of information on the reviewers' profiles and the reviewed hotels. We argue that the enriched data can gain insights on the factors that most influence consumers when composing reviews (e.g., if the appreciation for a certain kind of hotel is tied to specific users' profiles). Thus, we apply statistical analyses to reveal if there are specific characteristics of reviewers (almost) always related to specific characteristics of hotels. Our experiments are carried out on a very large dataset, consisting of around 190k hotel reviews, collected from the Tripadvisor website.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
