Are Yelp's tips helpful in building influential consumers?

• Main Author: Guerreiro, J.
• Publication Date: 2017
• Other Authors: Moro, S.
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10071/14296
• topic: eWOM; Online reviews; Fandom; Text mining; Support vector machine

In the cluttered environment of online reviews, consumers frequently have to choose the most trustworthy reviewers to help them in their purchasing decision. Such reviewers are influential in their community and co-create value among their peers. The current research note studies the antecedents of fandom, particularly if contents of the message written by the reviewers predict the number of fans they might have in the future. 27,097 tips written by 16,334 users of Yelp are structured using text mining and a support vector machine algorithm is used to study the accuracy of such relation. Results show that tips which may help consumers to avoid the service and tips that highlight the positive elements of the service are the most relevant in predicting the number of fans. Findings may help managers to understand which type of messages may increase the reviewer's number of fans, thus increasing their influence in the network.