Assessing gastronomic tourism using machine learning approach: The case of google review
Bibliographic record
Abstract
This study aims to evaluate tourists' reviews of gastronomy tourism expressed in Google reviews according to the CAC model (Cognitive, Affective, and Conative), and to examine the inter-correlations between CAC model components. The study was applied to traditional restaurants in Amman downtown. The research then extracts the main themes from the textual reviews as well as a sentiment score of an affective image of traditional Amman downtown restaurants. The results of machine learning experiments suggest that the proposed approach can identify traditional restaurant reviews in Amman downtown into CAC model components. The results also show that the Random Forest algorithm performed best in the cognitive and cognitive dimensions, whereas the Neural Network algorithm performed best in the affective dimension. ML classifier revealed that most of the reviews were classified as cognitive (such as the type of food, and services) while the remaining reviews were classified as affective (such as pleasure and arousal) and conative (such as intention to recommend, and positive word of mouth) respectively. The highest probability of the cognitive components was the traditional food topic reflecting the unique image of Jordanian traditional food. Affective images formed by users were mainly positive emotions, indicating that the destination image spread well.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".