A Predictive Model Based on TripAdvisor Textual Reviews: Early Destination Recommendations for Travel Planning

Abstract

Although many studies have considered the effects of online reviews on tourists’ decisions, none have directly investigated how to leverage open data analyses to create early choice sets and facilitate destination planning. This paper illustrates how salient characteristics can be mined from the shared experiences embedded in review data and incorporated into a predictive model to build a travel counseling approach. The model is designed by first defining a prediction-based mechanism from online reviews and then generating a multinomial classification problem on all candidate destinations of interest. The model is implemented by applying Natural Language Processing (NLP) and Deep Learning (DL) technologies to review textual features. The model is validated using 75,315 reviews from TripAdvisor along with destinations from 257 U.S. national parks. Empirical results indicate a best classification accuracy of 67%, outperforming two previous approaches. Findings shed light on how to exploit past tourists’ experiences to generate early destination recommendations to identify items for choice sets and reduce tourists’ travel-planning effort. Theoretical and managerial implications regarding social media analytics are provided based on online review meta-data in touristic management.