mlsanityData to 7 Oct 2026

Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews

arXiv 2602.12778 ↗First posted 13 Feb 2026cs.CL cs.LGcited by 0

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Abstract (arXiv)

This study advances aspect-based sentiment analysis (ABSA) for Persian-language user reviews in the tourism domain, addressing challenges of low-resource languages. We propose a hybrid BERT-based model with Top-K routing and auxiliary losses to mitigate routing collapse and improve efficiency. The pipeline includes: (1) overall sentiment classification using BERT on 9,558 labeled reviews, (2) multi-label aspect extraction for six tourism-related aspects (host, price, location, amenities, cleanliness, connectivity), and (3) integrated ABSA with dynamic routing. The dataset consists of 58,473 preprocessed reviews from the Iranian accommodation platform Jabama, manually annotated for aspects and sentiments. The proposed model achieves a weighted F1-score of 90.6% for ABSA, outperforming baseline BERT (89.25%) and a standard hybrid approach (85.7%). Key efficiency gains include a 39% reduction in GPU power consumption compared to dense BERT, supporting sustainable AI deployment in alignment with UN SDGs 9 and 12. Analysis reveals high mention rates for cleanliness and amenities as critical aspects. This is the first ABSA study focused on Persian tourism reviews, and we release the annotated dataset to facilitate future multilingual NLP research in tourism.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 38 references that are in the map. First posted on arXiv 13 Feb 2026.

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12 of 38 references.

References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  5. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  6. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  7. 2020ParsBERT: Transformer-based Model for Persian Language Understandingcited by 92
  8. 2024Turn Waste into Worth: Rectifying Top-k Router of MoEcited by 11
  9. 2021A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methodscited by 8
  10. 2020DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpuscited by 7
  11. 2020Exploiting BERT to improve aspect-based sentiment analysis performance on Persian languagecited by 5
  12. 2024Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Informationcited by 1

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  1. 2026Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs
  2. 2026AnnoABSA: A Web-Based Annotation Tool for Aspect-Based Sentiment Analysis with Retrieval-Augmented Suggestions
  3. 2025From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models
  4. 2023On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training
  5. 2024Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models
  6. 2024DS2-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis
  7. 2024Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysis
  8. 2024Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models