Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews
Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews
复制标题
属性情感引导的在线评论用户意见总结
DOI：
10.1115/1.4055736
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发表时间：
2023
影响因子：
3.3
通讯作者：
Moghaddam, Mohsen
中科院分区：
文献类型：
--
作者：
Han, Yi;Nanda, Gaurav;Moghaddam, Mohsen
从在线评论中获得信息丰富的用户意见是创新产品设计和开发的关键成功因素。然而，用户评论的非结构化、嘈杂和冗长的本质，往往使大规模的需求查找复杂化，而这种格式对设计师有用，同时又不会丢失重要信息。摘要文本摘要的最新进展为系统地从在线评论中生成意见摘要创造了机会，从而为产品设计和开发的早期阶段提供信息。然而，两个知识缺口阻碍了意见摘要方法在实践中的适用性。首先，缺乏正式的机制来指导不同类别的产品属性和用户情绪的生成过程。其次，抽象摘要模型的监督训练所需的带注释的训练数据集通常很难创建，而且成本很高。本文通过以下方式解决了这些差距：（1）设计一个有效的计算框架，用于在特定产品属性和情感极性的指导下进行抽象意见摘要，以及（2）自动生成一个合成训练数据集，该数据集捕获各种程度的粒度和极性。开发了一种分层多实例属性-情感推理模型，用于组装高质量的合成数据集，该数据集用于微调预训练的语言模型以生成抽象摘要。从三个主要的电子商务零售商店的服装和鞋类产品的大型数据集上进行的数值实验表明，性能，可行性和潜力的开发框架。在以用户为中心的设计领域的自动化意见摘要的未来探索提供了几个方向。
Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale need finding in a format useful for designers without losing important information. Recent advances in abstractive text summarization have created the opportunity to systematically generate opinion summaries from online reviews to inform the early stages of product design and development. However, two knowledge gaps hinder the applicability of opinion summarization methods in practice. First, there is a lack of formal mechanisms to guide the generative process with respect to different categories of product attributes and user sentiments. Second, the annotated training datasets needed for supervised training of abstractive summarization models are often difficult and costly to create. This article addresses these gaps by (1) devising an efficient computational framework for abstractive opinion summarization guided by specific product attributes and sentiment polarities, and (2) automatically generating a synthetic training dataset that captures various degrees of granularity and polarity. A hierarchical multi-instance attribute-sentiment inference model is developed for assembling a high-quality synthetic dataset, which is utilized to fine-tune a pretrained language model for abstractive summary generation. Numerical experiments conducted on a large dataset scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, feasibility, and potentials of the developed framework. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered design.