Abstract
This paper examines the sentiments and opinions of e-scooter riders in Windsor, Ontario, highlighting key issues and concerns they have expressed. It involved text mining of feedback collected over a six-month pilot program (May to October 2021) using dictionary-based analysis. Analysis of monthly word frequencies in rider feedback revealed fluctuations, with June, July, and August showing higher correlations with May—the initial pilot month—compared to September and October. This indicates a fading novelty associated with e-scooters in the city. Although monthly sentiments varied significantly, the overall sentiment in May and June remained positive. The most common words contributing to positive sentiment included fun, awesome, and nice, while negative sentiments were largely represented by words such as slow, broken, and throttle. Feedback reveals that riders primarily regard e-scooters as a source of leisure rather than functional transportation. Correlation analysis of words linked to negative sentiments identified terms like “flat-tire” and “broken throttle,” which emphasize significant concerns regarding e-scooter maintenance practices in Windsor. The findings underscore the need for a data-sharing policy and maintenance regulations while recommending a governance framework for e-scooters to ensure their sustainable benefits. It demonstrates that even with a limited feedback sample during the pilot phase of shared e-scooter implementation, dictionary-based opinion and sentiment analysis can yield valuable insights into rider concerns, guiding immediate policy needs and fostering the functional use of e-scooters as a transportation option.
Keywords:
E-scooter, NPU, Opinion and Sentiment Analysis, Riders’ Feedback, Text Mining, User Generated Content, YOLOv11References
Issue
Copyright & License
This work is licensed under a Creative Commons Attribution 4.0 International License.