Travellers segmentation and choice prediction through online reviews: the case of Wellingtons Hotels in New Zealand

• Main Authors: Ahani, Ali, Nilashi, Mehrbakhsh, Ibrahim, Othman
• Format: Article
• Language: English
• Published: Journal of Soft Computing and Decision Support Systems 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/88348/1/OthmanIbrahim2019_TravellersSegmentationandChoicePrediction.pdf; http://eprints.utm.my/id/eprint/88348/; https://jscdss.com/index.php/files/article/download/209/pdf_249

Customer choice and segmentation through online reviews can help hotels to improve their marketing strategy development. Nevertheless, old-style approaches are unproductive in analysing online data generated by customers because of size, dissimilar proportions and structures of online review data. Therefore, this research aims to develop a method for 5-star hotels segmentation and travellers’ choice forecast through online reviews analysis using machine learning methods. Assessment of method was directed through the gathering of data from travellers’ ratings of Wellington’s 5-star hotels on different features in TripAdvisor. Results confirm that the projected hybrid machine learning approaches can be applied as a progressive recommender mediator for 5-star hotel segmentation by applying ‘big data’ obtained from online social media settings.