Deriving Knowledge from E-Scooter Riders’ Feedback at Pilot Study Stage: Case for a City in Ontario, Canada
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".