Text mining approach to explore dimensions of national parks visitors’ experience and satisfaction using online customer reviews
Bibliographic record
Abstract
Natural parks are gaining global popularity with millions of visits per year. However, studies evaluating park visitors' experiences, satisfaction, and motivations are limited to traditional methods, such as direct observations, spatial analysis using global positioning system (GPS) trackers, interviews, surveys, and focus groups. As an alternative to these conventional methods, user-generated content (UGC) provides available, easily accessible, and consumers' reliable recent experiences with services. This study investigated visitors' reviews of selected Canadian national parks to explore the dimensions of the visitors' quality of experience and the drivers of satisfaction based on online ratings on the Trip Advisor website. The analysis yielded various topics ranging from visitors' pleasant feelings about trails, mountain views, and water activities to their unpleasant experiences regarding food, crowds, lineups, and parking lots. Besides, analysis of the reviews based on the reviewers' start ratings highlighted the most important drivers of satisfaction and dissatisfaction between them. Findings suggest that topics related to schedule and weather-related hassles; food, tickets, and shopping experiences; and visitor information center and exhibitions were among the most distinguishes dissatisfied visitors (1- and 2-star) from others. On the other hand, positive vibes, expressiveness, photography opportunities, and daytime and weather experience were the principal determinants of visitors' satisfaction (4- and 5-star).
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".