面向未来旅游体验的基于方面的情感分析：一种针对波斯语用户评论的BERT-MoE框架
Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews
AI总结：
本文提出一种BERT-MoE框架，用于波斯语旅游评论的基于方面的情感分析，实现90.6%的ABSA F1分数，同时提升计算效率，支持可持续AI发展。
摘要：
本研究推进了针对波斯语用户评论在旅游领域的基于方面的情感分析（ABSA），解决了低资源语言的挑战。我们提出了一种基于BERT的混合模型，结合Top-K路由和辅助损失，以缓解路由崩溃并提高效率。该流程包括：（1）使用BERT对9558条标注评论进行整体情感分类；（2）对六个旅游相关方面（宿主、价格、位置、设施、清洁度、连接性）进行多标签方面提取；（3）集成ABSA与动态路由。数据集由来自伊朗住宿平台Jabama的58473条预处理评论组成，人工标注了方面和情感。所提出的模型在ABSA上实现了90.6%的加权F1分数，优于基线BERT（89.25%）和标准混合方法（85.7%）。关键的效率提升包括与密集BERT相比GPU电力消耗减少了39%，支持可持续AI部署，符合联合国SDGs 9和12。分析揭示了清洁度和设施的高提及率作为关键方面。这是首个专注于波斯旅游评论的ABSA研究，我们发布标注数据集以促进未来旅游领域的多语言NLP研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
