Description

• Title:
  Exploring memorable gastronomic experiences: Automatic topic modelling of TripAdvisor reviews
• Creator:
  Nowacki, Marek;
  Stasiak, Andrzej;
  Niezgoda, Agnieszka
• Contributor:
  WSB Merito University in Poznań;
  University of Lodz;
  Poznań University of Economics and Business
• Description:
  The article aims to identify memorable gastronomic experiences reported online and verify their relationships with the type of cuisine served and restaurant location. This study used text mining, LDA, Pearson’s chi-squared test and sentiment analysis. All 48,378 English reviews posted by TripAdvisor users concerning 155 restaurants in Krakow were scraped. Eight features that characterise MGEs were identified (service/staff, atmosphere, cuisine/food (taste), drinks, local specialities, location/setting, price & value and table booking). There are statistically significant differences in the frequency of the topic experiences depending on the location of restaurants in the city.
• Object availability:
  free access
• Rights: http://creativecommons.org/licenses/by-nc-nd/4.0 ;
  Attribution-NonCommercial-NoDerivatives 4.0 Międzynarodowe
• Date:
  2023.12.11;
  2023
• Type:
  Article
• Language:
  English
• Format:
  application/pdf
• Publisher:
  Institute of Geography and Spatial Organization Polish Academy of Sciences
• Relation:
  Geographia Polonica;3;
  96
• Subject:
  food tourism;
  culinary experiences;
  Krakow;
  restaurant management;
  Latent Dirichlet Allocation;
  sentiment analysis
• Identifier:
  Nowacki, M., Stasiak, A., & Niezgoda, A. (2023). Exploring memorable gastronomic experiences: Automatic topic modelling of TripAdvisor reviews. Geographia Polonica, 96(3), 381-399.;
  0016-7282;
  http://hdl.handle.net/11089/48692 ;
  2300-7362;
  10.7163/GPol.0261
• Data provider:
  Repozytorium Uniwersytetu Łódzkiego
• Can I use it?:
  seek permission
• Type:
  image