Dynamic product competitive analysis based on online reviews

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems Pub Date : 2024-06-10 DOI:10.1016/j.dss.2024.114268

Lu Zheng , Lin Sun , Zhen He , Shuguang He

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引用次数: 0

Abstract

Competitive intelligence is vital for enterprises to survive in the market. Recently, online reviews have gained popularity among enterprises and researchers as a means to acquire timely and precise competitive insights. However, extant studies overlook the evolution of competitive information because they do not account for the variation of online reviews and products. In this research, we propose a method for dynamic competitive analysis by concentrating on the changes in products and online reviews. First, products and their related online reviews are analyzed via Dynamic Topic Model to derive product features mentioned in different slices. Second, we use sentiment analysis to estimate product performance and transfer the results into a product competitive relation network. Third, we implement competitive analysis from the perspectives of products and markets based on competitiveness propagation. By tracking the evolution of competitive relations among products, we discover competitors and glean more competitive insights. Lastly, a case study of laptops is used for validation. Experimental results indicate that our method is effective in capturing evolving and potential competitive relations among products.

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基于在线评论的动态产品竞争分析

竞争情报对于企业在市场中生存至关重要。最近，在线评论作为一种及时、准确地获取竞争洞察力的手段，受到了企业和研究人员的青睐。然而，现有的研究由于没有考虑到在线评论和产品的变化而忽视了竞争信息的演变。在本研究中，我们提出了一种通过关注产品和在线评论的变化来进行动态竞争分析的方法。首先，通过动态主题模型分析产品及其相关在线评论，得出不同切片中提及的产品特征。其次，我们利用情感分析来估计产品性能，并将结果转移到产品竞争关系网络中。第三，我们基于竞争力传播，从产品和市场的角度实施竞争力分析。通过跟踪产品间竞争关系的演变，我们发现了竞争对手，并获得了更多的竞争洞察。最后，我们使用笔记本电脑案例进行验证。实验结果表明，我们的方法能有效捕捉产品间不断演变和潜在的竞争关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Decision Support Systems 工程技术-计算机：人工智能

CiteScore

14.70

自引率

6.70%

发文量

119

审稿时长

13 months

期刊介绍： The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).