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 |
CORE Recommender
SCOPUSTM
Citations
7
checked on Mar 14, 2024
Page view(s)
339
Last Week
0
0
Last month
3
3
checked on Nov 21, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.