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Bike-sharing has globally emerged as an alternative travel mode for trips that are longer to walk but shorter to drive. Previous studies have used either the actual ridership data or survey responses from users to understand the public perception about bike-sharing systems. Where the actual ridership data is hard to obtain, survey-based studies limit respondents’ ability to express their views by relying on structured questionnaires (e.g., Likert scale). To this end, we contribute with the first application of a text network approach by analyzing the open-ended text responses from over 700 Seattle residents regarding their perceptions about the characteristics of docked and dockless bike-sharing systems. The text network approach enables the analysis of the open-ended text responses by creating a network based on the frequency and co-occurrence of keywords in a sentence. Our empirical analysis shows that Seattle …