Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19244
Title: Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing
Authors: Christodoulou, Evripides 
Gregoriades, Andreas 
Pampaka, Maria 
Herodotou, Herodotos 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Topic modelling;Sentiment analysis;Decision tree;Tourists’ reviews
Issue Date: 16-May-2020
Source: 32nd International Conference on Advanced Information Systems Engineering, 8-12 June 2020, Grenoble, France
Conference: International Conference on Advanced Information Systems Engineering 
Abstract: This paper applies a smart tourism approach to tourist destination marketing campaigns through the analysis of tourists’ reviews from TripAdvisor to identify significant patterns in the data. The proposed method combines topic modelling using Structured Topic Analysis with sentiment polarity, information on culture, and purchasing power of tourists for the development of a Decision Tree (DT) to predict tourists’ experience. For data collection and analysis, several custom-made python scripts were used. Data underwent integration, cleansing, incomplete data processing, and imbalance data treatments prior to being analysed. The patterns that emerged from the DT are expressed in terms of rules that highlight variable combinations leading to negative or positive sentiment. The generated predictive model can be used by destination management to tailor marketing strategy by targeting tourists who are more likely to be satisfied at the destination according to their needs.
URI: https://hdl.handle.net/20.500.14279/19244
ISBN: 9783030491642
DOI: 10.1007/978-3-030-49165-9_9
Rights: © Springer Nature
Type: Book Chapter
Affiliation : Cyprus University of Technology 
University of Manchester 
Publication Type: Peer Reviewed
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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