Reviews of monuments have a huge impact on the decision-making of tourists, determining whether or not those monuments will be visited. The will to analyze textually these reviews leads to performing Sentiment Classification on macro-level topics covered by the reviews, to provide a clear idea of what people think about all the different aspects of a site of interest. This paper tackles the problem of extracting topics from big data in the form of textual reviews employing Topic Modeling techniques. As an application, all the reviews of the Colosseum between January 2004 to mid-March 2022 have been extracted from the TripAdvisor’s website and analyzed.
Topic Modeling for the travel and tourism industry: classical and innovative methods compared / DI MARI, Fabrizio. - (2023), pp. 1105-1110. (Intervento presentato al convegno Statistical Learning, Sustainability and Impact Evaluation tenutosi a Ancona).
Topic Modeling for the travel and tourism industry: classical and innovative methods compared
Fabrizio Di Mari
Primo
2023
Abstract
Reviews of monuments have a huge impact on the decision-making of tourists, determining whether or not those monuments will be visited. The will to analyze textually these reviews leads to performing Sentiment Classification on macro-level topics covered by the reviews, to provide a clear idea of what people think about all the different aspects of a site of interest. This paper tackles the problem of extracting topics from big data in the form of textual reviews employing Topic Modeling techniques. As an application, all the reviews of the Colosseum between January 2004 to mid-March 2022 have been extracted from the TripAdvisor’s website and analyzed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
