Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing

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.

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

“…The study on TripAdvisor used deep learning models based on the Myers-Briggs Type Indicator (MBTI) to discern consumers' personalities from electronic word-of-mouth (e-WOM). Findings linked specific discussion themes to personality traits, offering insights for personalized marketing messages and optimizing communication strategies [155].…”

“…[154] Amazon Investigated user satisfaction with physical activity trackers using sentiment analysis. [155] TripAdvisor Employed text classification and topic modeling to discern consumer personalities.…”

Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews

“…Retrieved data is often voluminous but of low value and has little direct usefulness in its raw form. It is the hidden information in the data that has value [44,45]. Retrieved data must be reorganized and stored according to clear field structures before DM can be carried out efficiently and accurately [46].…”

Exploiting User-Generated Content for Service Improvement: Case Airport Twitter Data

“…Topic modeling is a research method that has been used to discover topics and to group and identify terms in user-generated content where the topics are not known beforehand. Some of the topic modeling techniques used in the service quality literature of the travel and tourism industry include: Structured Topic Analysis (STA) in tourism destination marketing [21], non-negative matrix factorization (NMF) in airlines and Latent Dirichlet Allocation (LDA) in hotels [22] and in airports [23]. This research employs the Latent Dirichlet Allocation (LDA) [24] .…”