Analysis of travellers' online reviews in social networking sites using fuzzy logic approach

• Main Authors: Nilashi, Mehrbakhsh, Yadegaridehkordi, Elaheh, Ibrahim, Othman, Samad, Sarminah, Ahani, Ali, Sanzogni, Louis
• Format: Article
• Published: Springer Berlin Heidelberg 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/88577/; http://dx.doi.org/10.1007/s40815-019-00630-0

Social media and digital technology have had significant contributions and impacts on the hospitality and accommodation businesses. Online traveller reviews have been rich sources of information for the traveller’s decision-making process in social media websites. TripAdvisor, a popular travel review site and social media platform, is mainly developed as a free business consultation service to help the travellers to make right decisions in their trips. The aim of this research is to use the multi-criteria ratings provided by the travellers in social media networking sites for developing a new recommender system for hotel recommendations in e-tourism platforms. We extend the crisp-based multi-criteria algorithms to fuzzy-based multi-criteria algorithms for finding the similarities between the travellers based on their provided ratings. To develop the recommendation method, we use clustering and prediction machine learning techniques. We evaluate the recommendation system on TripAdvisor data. Our experiments confirm that the use of clustering and prediction machine learning with the aid of fuzzy-based recommendation algorithms can significantly improve the quality of recommendations in tourism domain.