Exploring Online Arabic Complaints in Hotel Reviews on TripAdvisor: A Discourse-Pragmatic Study
Bibliographic record
Abstract
Although much work has explored Arabic complaints in face-to-face (FTF) communication, the subject has received less scholarly attention in computer mediated communication (CMC). In response to the lack of studies on online Arabic complaints, the present study aimed to identify the types of speech acts employed in Arabic complaints on TripAdvisor, the specific topics evaluated in negative reviews, and the adjectives used to convey the reviewers’ evaluations. The study was conducted on hotels in Saudi Arabia, with a sample comprising 246 reviews of 35 hotels in Riyadh, Al-Khobar, and Jeddah. Only 5-star hotels were included. The data were collected manually and analyzed qualitatively and quantitatively using Microsoft Excel. The results showed that when writing negative reviews on TripAdvisor, Arabs used various speech acts to express their complaints; the most frequently used were retrospective speech acts that included mostly negative evaluations with some positive evaluations. Additionally, the topics most frequently evaluated negatively were services, interpersonal relations, and accommodation; such negative evaluations featured various adjectives with some adverbs to intensify the negative review. Regarding positive evaluations, location was the most frequent positively evaluated aspect, followed by services and accommodation. The results also demonstrated that Arabs rarely used opening and closing speech acts in their negative reviews on TripAdvisor. Finally, the study’s limitations and suggestions are discussed in this paper for the benefit of further research.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".