Analyzing TripAdvisor reviews of wine tours: an approach based on text mining and sentiment analysis

Abstract

PurposeWine packaged tours as a specific aspect of wine tourism have so far been neglected in research, for this reason, the purpose of this study is to study the key elements for the success of the wine tour in Tuscany (Italy), evaluating the points of strength and weakness.Design/methodology/approachThe study combines approaches of text mining, sentiment analysis and natural language processing, drawing on data from the TripAdvisor platform, obtaining through an automatic procedure 9,616 reviews from 600 tours in the years 2010–2020.FindingsThe authors identified six elements of successful wine tours expressed by research subjects: tour guide; logistical aspects; the quality of the wine; the quality of the food; complementary tourist and recreational activities; the landscape and historic villages. The key strength associated with success was the integration of the leading wine product with food, landscape and historic villages, while the main criticisms were concerned with the organization and planning of the tour. Furthermore, the tour guide also plays a fundamental role in consumer satisfaction.Research limitations/implicationsThe limitations of the method were linked to the origin of the data used. The main one is that TripAdvisor does not allow you to have social and personal information about the tourist who wrote the review; therefore, the methods are substantially complementary to the traditional survey through questionnaires.Practical implicationsThe proposed model can be used both by professionals to improve the quality of their products and by policymakers to promote the territorial development of quality wine-growing areas.Social implicationsThe proposed model can be useful for policymakers to promote the territorial development of quality wine-growing areas.Originality/valueThe methodology we tested is easily transferable to many countries and to the authors’ knowledge, for the first time attempts to combine multidimensional scaling, sentiment analysis and natural language processing approaches.