TY - GEN
T1 - Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing
AU - Christodoulou, Evripides
AU - Gregoriades, Andreas
AU - Pampaka, Maria
AU - Herodotou, Herodotos
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Decision tree
KW - Sentiment analysis
KW - Topic modelling
KW - Tourists’ reviews
UR - http://www.scopus.com/inward/record.url?scp=85085513758&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49165-9_9
DO - 10.1007/978-3-030-49165-9_9
M3 - Conference contribution
AN - SCOPUS:85085513758
SN - 9783030491642
T3 - Lecture Notes in Business Information Processing
SP - 95
EP - 108
BT - Advanced Information Systems Engineering Workshops - CAiSE 2020 International Workshops, Proceedings
A2 - Dupuy-Chessa, Sophie
A2 - Proper, Henderik A.
PB - Springer London
T2 - 2nd International Workshop on Key Enabling Technologies for Digital Factories, KET4DF 2020 and the 1st International Workshop on Information Systems Engineering for Smarter Life, ISESL 2020, associated with the 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020
Y2 - 8 June 2020 through 12 June 2020
ER -