Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29867
Title: Explaining tourist revisit intention using natural language processing and classification techniques
Authors: Gregoriades, Andreas 
Pampaka, Maria 
Herodotou, Herodotos 
Christodoulou, Evripides 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Explainable machine learning;Negation detection;Revisit intention;Text classification;Topic modelling
Issue Date: 1-Jan-2023
Source: Journal of Big Data, 2023, vol.10, iss. 1
Volume: 10
Issue: 1
Journal: Journal of Big Data 
Abstract: Revisit intention is a key indicator of business performance, studied in many fields including hospitality. This work employs big data analytics to investigate revisit intention patterns from tourists’ electronic word of mouth (eWOM) using text classification, negation detection, and topic modelling. The method is applied on publicly available hotel reviews that are labelled automatically based on consumers’ intention to revisit a hotel or not. Topics discussed in revisit-annotated reviews are automatically extracted and used as features during the training of two Extreme Gradient Boosting models (XGBoost), one for each of two hotel categories (2/3 and 4/5 stars). The emerging patterns from the trained XGBoost models are identified using an explainable machine learning technique, namely SHAP (SHapley Additive exPlanations). Results show how topics discussed by tourists in reviews relate with revisit/non revisit intention. The proposed method can help hoteliers make more informed decisions on how to improve their services and thus increase customer revisit occurrences.
URI: https://hdl.handle.net/20.500.14279/29867
ISSN: 21961115
DOI: 10.1186/s40537-023-00740-5
Rights: © The Author(s)
Type: Article
Affiliation : Cyprus University of Technology 
The University of Manchester 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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