面向产品设计需求分析和细分的社会倾听:基于用户评论挖掘的图分析方法。

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-12-01 Epub Date: 2023-09-04 DOI:10.1089/big.2022.0021
Xinjun Lai, Guitao Huang, Ziyue Zhao, Shenhe Lin, Sheng Zhang, Huiyu Zhang, Qingxin Chen, Ning Mao
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引用次数: 0

摘要

本研究通过社交媒体的在线评论来调查客户的产品设计需求,并将这些需求快速转化为产品设计规范。首先,提出了指数判别滚雪球抽样方法生成乘积相关子网络;其次,利用自然语言处理(NLP)对用户生成的评论进行挖掘,并采用Graph SAmple和aggreGatE方法将用户的节点邻域信息嵌入到网络中,共同定义用户的角色;聚类用于市场和产品模型分割。最后,提出了一种基于条件随机场的深度学习双向长短期记忆框架。提出了一种评论频率逆变组频率指标,量化所有用户组对不同产品功能的各种规格的正面和负面意见。一个智能手机设计分析的案例研究采用了来自中国大型在线社区百度贴吧的数据。11层社会关系被滚雪球抽样,有14018个用户和30803条评论。与传统方法相比,该方法获得了更合理的用户组聚类结果。通过我们的方法,可以立即识别用户群体对规格的主导喜欢和不喜欢,并立即揭示不同用户群体对产品功能的相似和不同偏好。还讨论了管理和工程方面的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Listening for Product Design Requirement Analysis and Segmentation: A Graph Analysis Approach with User Comments Mining.

This study investigates customers' product design requirements through online comments from social media, and quickly translates these needs into product design specifications. First, the exponential discriminative snowball sampling method was proposed to generate a product-related subnetwork. Second, natural language processing (NLP) was utilized to mine user-generated comments, and a Graph SAmple and aggreGatE method was employed to embed the user's node neighborhood information in the network to jointly define a user's persona. Clustering was used for market and product model segmentation. Finally, a deep learning bidirectional long short-term memory with conditional random fields framework was introduced for opinion mining. A comment frequency-invert group frequency indicator was proposed to quantify all user groups' positive and negative opinions for various specifications of different product functions. A case study of smartphone design analysis is presented with data from a large Chinese online community called Baidu Tieba. Eleven layers of social relationships were snowball sampled, with 14,018 users and 30,803 comments. The proposed method produced a more reasonable user group clustering result than the conventional method. With our approach, user groups' dominating likes and dislikes for specifications could be immediately identified, and the similar and different preferences of product features by different user groups were instantly revealed. Managerial and engineering insights were also discussed.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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