一种可扩展的无监督框架用于多方面标注多语言和多领域评论数据
A Scalable Unsupervised Framework for multi-aspect labeling of Multilingual and Multi-Domain Review Data
AI总结：
本文提出了一种可扩展的无监督框架，用于多语言和多领域评论数据的多方面标注，通过实验验证了其在大规模数据处理中的有效性与一致性。
摘要：
有效分析在线评论数据对于各行业至关重要。然而，许多现有研究局限于特定领域和语言或依赖于需要大规模标注数据集的监督学习方法。为了解决这些限制，我们提出了一种多语言、可扩展且无监督的框架，用于跨领域方面检测。该框架旨在对多语言和多领域评论数据进行多方面标注。在本研究中，我们对涵盖各种领域的韩语和英语评论数据集应用自动标注，并通过广泛的实验评估生成标签的质量。首先通过聚类提取方面类别候选者，然后使用负采样将每条评论表示为方面感知的嵌入向量。为了评估该框架，我们进行了多方面标注，并对几种预训练语言模型进行微调以测量自动生成标签的有效性。结果表明，这些模型实现了高性能，证明了标签适合训练。此外，与公开可用的大型语言模型的比较突显了该框架在处理大规模数据时的优越一致性和可扩展性。一项人工评估也确认了自动标签的质量与人工创建的标签相当。本研究展示了稳健的多方面标注方法的潜力，这种方法克服了监督方法的限制，并能够适应多语言、多领域的环境。未来研究将探索自动评论摘要和人工智能代理的整合，以进一步提高评论分析的效率和深度。
英文摘要：
Effectively analyzing online review data is essential across industries. However, many existing studies are limited to specific domains and languages or depend on supervised learning approaches that require large-scale labeled datasets. To address these limitations, we propose a multilingual, scalable, and unsupervised framework for cross-domain aspect detection. This framework is designed for multi-aspect labeling of multilingual and multi-domain review data. In this study, we apply automatic labeling to Korean and English review datasets spanning various domains and assess the quality of the generated labels through extensive experiments. Aspect category candidates are first extracted through clustering, and each review is then represented as an aspect-aware embedding vector using negative sampling. To evaluate the framework, we conduct multi-aspect labeling and fine-tune several pretrained language models to measure the effectiveness of the automatically generated labels. Results show that these models achieve high performance, demonstrating that the labels are suitable for training. Furthermore, comparisons with publicly available large language models highlight the framework's superior consistency and scalability when processing large-scale data. A human evaluation also confirms that the quality of the automatic labels is comparable to those created manually. This study demonstrates the potential of a robust multi-aspect labeling approach that overcomes limitations of supervised methods and is adaptable to multilingual, multi-domain environments. Future research will explore automatic review summarization and the integration of artificial intelligence agents to further improve the efficiency and depth of review analysis.
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。
