Abstract
The Internet in general and social media in particular have changed the way travellers search for information, interact and communicate with companies and among their peers. Through social media, travellers are not only content consumers but also producers who provide detailed evaluations of their experiences in a destination. Given the abundance of hotel reviews, managers need cost effective, time saving and scalable systems to identify relevant customer evaluations. This text mining study presents insights into the analysis of reviews posted on TripAdvisor. A penalized Support Vector Machine identifies keywords representative for the most positive and negative reviews. These terms can be used as an early warning system by managers to efficiently monitor the online dialogue on their hotel. The benefit of the suggested approach is the selection of the most important terms regarding the weight of the term and not regarding mere term frequency.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
