Intelligent data-driven acquisition method for user requirements

Q1 Social Sciences

Personal and Ubiquitous Computing Pub Date : 2024-04-20 DOI:10.1007/s00779-024-01804-w

Zhenwei You, Jian Liu, Tingting Yang, Jiagang Cao, Wei-Chen Chang

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Abstract

Consumer behavior has changed due to digitization. Online shoppers now refer to user reviews containing comprehensive data produced in real-time, which can be used to determine users’ needs. This paper combines Kansei engineering and natural language processing techniques to extract information on users’ needs from online reviews and provide guidance for subsequent product improvements and development. A crawler tool was used to collect a large number of online reviews for a target product. Frequency analysis was then applied to the text to filter out the product components worth analyzing. The results were categorized and aggregated by experts before sentiment analysis was performed on statements containing the selected adjectives. Finally, the user needs identified could be inputted to Kansei engineering for further product design. This paper verifies the merit of the above method when applied to the mountain bike product category on Amazon. The method proved to be a quick and efficient way to attain accurate product evaluations from end-users and thus represents a feasible approach to intelligently determining user preferences.

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用户需求的智能数据驱动采集方法

数字化使消费者行为发生了变化。现在，网上购物者会参考用户评论，这些评论包含实时生成的综合数据，可用于确定用户需求。本文结合康采恩工程学和自然语言处理技术，从在线评论中提取用户需求信息，为后续产品改进和开发提供指导。本文使用爬虫工具收集了大量目标产品的在线评论。然后对文本进行频率分析，筛选出值得分析的产品组成部分。专家对结果进行分类和汇总，然后对包含选定形容词的语句进行情感分析。最后，确定的用户需求可以输入到 Kansei 工程中，以便进行进一步的产品设计。本文验证了上述方法应用于亚马逊山地自行车产品类别时的优点。事实证明，该方法能快速有效地获得最终用户对产品的准确评价，因此是智能确定用户偏好的可行方法。

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

Personal and Ubiquitous Computing 工程技术-电信学

CiteScore

6.60

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Personal and Ubiquitous Computing publishes peer-reviewed multidisciplinary research on personal and ubiquitous technologies and services. The journal provides a global perspective on new developments in research in areas including user experience for advanced digital technologies, the Internet of Things, big data, social technologies and mobile and wearable devices.