Assessing Tourist Interest Based on Gender Perceptions in Kayutangan Heritage through Sentiment Analysis of Google Point of Interest (POI)
Bibliographic record
Abstract
This study evaluates tourist interest at the Kayutangan Heritage area in Malang City based on gender perceptions through sentiment analysis of reviews on Google Point of Interest (POI). The research employs a big data approach by collecting reviews from Google Maps using web scraping techniques and processing them with the TextBlob algorithm to classify sentiments into positive, neutral, and negative categories. A total of 2,198 reviews were analyzed from six food and beverage (F&B) points across six spatial clusters of the Kayutangan Heritage area. The data were divided into two datasets—overall and gender-labeled—to identify sentiment tendencies among male and female tourists. The results indicate that female tourists tend to prefer Café Lafayette (Cluster 1.A), which offers women-friendly facilities such as separate restrooms and prayer rooms, with a positive sentiment of 67.6%. Meanwhile, male tourists are more interested in Kedai Sedjiwa (Cluster 2.B), which provides a clean environment, modern architecture, and a comfortable ambiance, generating 77.1% positive sentiment. The findings support Chebli et al.’s (2020) model of gender-based tourist preferences, demonstrating that men and women prioritize different elements of a destination. This study contributes to the development of gender-inclusive tourism strategies and data-driven management for heritage destinations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".