A Framework for Aspect-Based Sentiment and Opinion Mining in Persian Language: Dataset Creation and Domain Application in Hotel Reviews
Bibliographic record
Abstract
This paper presents a comprehensive framework for aspect-based sentiment and opinion mining in the Persian language, addressing the scarcity of resources and tools for low-resource languages. The proposed framework, termed RSAD (Recursive Spam-Aware Deep), integrates multiple modules including subjectivity detection, fake review identification, aspect extraction, and polarity scoring. To support this approach, a domain-specific Persian dataset of hotel reviews and a custom sentiment lexicon were created and annotated by native speakers. The hybrid architecture combines linguistic, behavioral, and deep semantic features, enabling robust handling of Persian morphology and free word order. Experimental results demonstrate that RSAD significantly outperforms baseline models (Lexicon-based, CNN, LSTM, and ParsBERT) across all major tasks, achieving an overall Fl-score of 0.91 and a ROC-ΛUC of 0.95 in fake review detection. The findings highlight the importance of language localization and modular design in developing scalable, interpretable sentiment analysis systems for Persian and other low-resource languages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".