How to predict explicit recommendations in online reviews using text mining and sentiment analysis

A text mining method was applied to online reviews to identify drivers of explicit recommendations and negative attitudes are triggers of negative direct recommendations, whereas positive feelings predict a positive recommendation in the body of the review.

Abstract

Opinions shared by peer travelers help tourists decrease the risks of making a poor decision. However, the increasing number of reviews per experience makes it difficult to read all reviews for an informed decision. Therefore, reviewers who make a personal and explicit recommendation of the services by using expressions such as “I highly recommend” or “don't recommend” may help consumers in their decision-making process. Such reviews suggest that the reviewer was satisfied to a point that (s)he would advise others to try or was unsatisfied and will for sure avoid coming back. The current research note explores what may drive reviewers to make direct endorsements in text. A text mining method was applied to online reviews to identify drivers of explicit recommendations. Lack of competences from the provider and negative attitudes are triggers of negative direct recommendations, whereas positive feelings predict a positive recommendation in the body of the review.