Shared mobility has gained widespread popularity as a result of its innovative business models. However, due to incomplete background checks of drivers, not only government authorities (i.e., transport authorities) but also the public has become concerned about service security. Currently, social networks such as Sina Weibo, a Chinese version of Twitter, provide an informative data source for capturing people's attitudes toward and perceptions of these mobility services. This dataset has the potential to help service platforms and regulators respond better to public demands and realize co-governance of the travel market. In this study, one month of Weibo data before and after a murder committed by a Didi ride-hailing driver was crawled. A hybrid approach integrating Latent Dirichlet Allocation (LDA) model and dictionary-based sentiment analysis was applied to extract topics receiving lots of attention and sentiment fractions, as well as to detect abnormal events. The results demonstrated that top-4 concerns of the public are service of ride-hailing, examination and responsibility definition of platforms, governmental regulations for market entry and capitalization, and case related description. People had negative attitudes toward ride-hailing platforms when talking about their lack of efficient management and crisis response strategies, while they were more positive about the business rivalries which are expected to enhance the quality of service in the travel market. Additionally, both events in the real world and interactions within social networks (i.e., #Boycott events) contributed to the public's sentiment. These findings can not only be useful for service platforms to detect public concerns about their services in real-time and to improve their business operations but also can be applied as a reference for government authorities to recognize multiple agents' interests for the co-governance of the urban mobility market.