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; 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.
2023
statistical learning; sustainability; impact evaluation
Topic Model, Natural Language Processing, Text Mining, Travel, Tourism
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
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; impact evaluation tenutosi a Ancona).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1713246
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