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.
Tourists' revisit has significant monetary benefits to destinations because the cost of retaining existing visitors is less than attracting new visitors. Re-visit intention is often based on tourists experience and satisfaction at a destination. An important aspect that influences the relationship between satisfaction and intention to revisit is the weather conditions at a destination given the increased frequency of heatwaves that strike summer holiday destinations over the summer months. This work applies natural language processing and classification techniques to evaluate the impact of weather information on revisit intention utilizing reviews from TripAdvisor and online weather data. Information retrieval techniques (Doc2Vec) are applied on online reviews collected during the summer months between 2010-2019 from tourists that visited Cyprus. Reviews are labeled as "revisits" or "neutral" based on their textual content. The labelled reviews dataset is enhanced with weather information based on the reviews' timestamp, such as temperature and humidity of tourists' country of origin and Cyprus at the time of the visit to the hotel/destination. To account for the influence of hotel infrastructure and available services to deal with heatwaves (i.e., climate-controlled), the training dataset included hotel star rating as an additional parameter. An ensemble gradient boosting tree classifier is trained utilizing the compiled dataset to predict revisit intention. The classifier is evaluated against the area under the curve. To interpret the classifier's inherent patterns, a popular machine learning interpretation technique is used, namely Shapley Additive Explanation (SHAP). Visualizations of the model using SHAP indicate that the heat index and weather difference between destination and country of origin influence revisit intention. Such preliminary insights are encouraging for further investigations with an end goal to develop a decision support system to assist destination managers during their target marketing campaigns.
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.
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