提出首个面向波斯语旅游评论的ABSA框架，提升低资源语言情感分析效果。
Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews
- 基于BERT与Top-K路由的混合模型，结合辅助损失缓解路由崩溃。
- 在58,473条评论上实现90.6%加权F1，GPU能耗降低39%。
- 首次构建波斯语旅游评论数据集，适合多语言文旅NLP研究者。
本研究推进波斯语旅游评论中的方面级情感分析（ABSA），应对低资源语言挑战。提出一种基于BERT的混合模型，采用Top-K路由和辅助损失，缓解路由崩溃并提升效率。流程包括：(1) 使用9,558条标注评论进行整体情感分类；(2) 提取六个旅游相关方面（主人、价格、位置、设施、清洁度、连通性）的多标签内容；(3) 实现动态路由集成的ABSA。数据集来自伊朗住宿平台Jabama，共58,473条预处理评论，人工标注了方面与情感。所提模型在ABSA任务上达到90.6%加权F1，优于基线BERT（89.25%）和标准混合方法（85.7%）。关键效率提升为相比密集BERT降低39%的GPU功耗，支持可持续AI部署，契合联合国可持续发展目标9与12。分析显示清洁度与设施提及率最高，为关键关注点。本研究是首个聚焦波斯语旅游评论的ABSA工作，已公开标注数据集，以促进未来多语言旅游NLP研究。
原文摘要 · Abstract (English)
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.
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