基于混合CSR方法的顾客评论比较关系挖掘

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Connection Science Pub Date : 2023-10-06 DOI:10.1080/09540091.2023.2251717
Song Gao, Hongwei Wang, Yuanjun Zhu, Jiaqi Liu, Ou Tang
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引用次数: 0

摘要

在线评论包含比较意见,揭示了相关产品的竞争关系,有助于确定产品在市场上的竞争力,并影响消费者的购买选择。类序列规则(Class Sequence Rule, CSR)方法是以往常用的评价比较关系识别方法,存在识别效率低、规则生成不准确等问题。本文对CSR方法进行了改进,提出了一种混合CSR方法,该方法利用依赖关系和词性来识别客户评论中频繁的序列模式,减少了人工干预,增强了关系挖掘过程中的序列规则。该方法的f值为84.67%,优于CSR和其他基于CSR的模型。在不同的实验中,我们发现该方法在生成序列模式时节省了时间和效率,因为依赖方向有助于减少序列长度。此外,该方法在隐式关系挖掘中也能很好地提取缺乏明显规则的比较信息。本研究采用最优CSR方法自动捕捉比较关系的深层特征,从而改进了显性和隐性比较关系的识别过程。
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Comparative relation mining of customer reviews based on a hybrid CSR method
Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations.
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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