Abstract
Online reviews are one of the most comprehensive and valuable sources of information actively sought by past, current, and potential customers. Especially for the service industry, such as restaurants, online reviews play a pivotal role in customers’ decision-making. Although the reviews contain rich information about the objective features and qualities of the restaurants, they can also reflect the particular qualities of an individual reviewer (perceptions and behaviors). This study aims to test people’s different reviewing behaviors on Chinese and French restaurants from two dimensions: users’ star ratings and users’ sentiments. We analyze data from Yelp - a platform that publishes crowdsourced reviews about businesses. Findings from this pilot study lay the foundation for a series of follow-up research. This work uncovers subtle information extracted from Yelp reviews and derives insights into how implicit discriminatory bias can manifest within online platforms. Furthermore, we should reduce bias when utilizing computational techniques on big data.
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.
