Jiang Huimin, Wu Xianhui, Sabetzadeh Farzad, Chan Kit Yan
School of Business, Macau University of Science and Technology, Macau, China.
Faculty of Business, City University of Macau, Macau, China.
Complex Intell Systems. 2023 Feb 21:1-11. doi: 10.1007/s40747-023-00986-9.
In online sales platforms, product design attributes influence consumer preferences, and consumer preferences also have a significant impact on future product design optimization and iteration. Online review data are the most intuitive feedback from consumers on products. Using the value of online review information to explore consumer preferences is the key to optimize the products, improve consumer satisfaction and meet consumer requirements. Therefore, the study of consumer preferences based on online reviews is of great importance. However, in previous research on consumer preferences based on online reviews, few studies have modeled consumer preferences. The models often suffer from the nonlinear structure and the fuzzy coefficients, making it challenging to build explicit models. Therefore, this study adopts a fuzzy regression approach with a nonlinear structure to model consumer preferences based on online reviews to provide reference and insight for subsequent studies. First, smartwatches were selected as the research object, and the sentiment scores of product reviews under different topics were obtained by text mining on the product online data. Second, a polynomial structure between product attributes and consumer preferences was generated to investigate the association between them further. Afterward, based on the existing polynomial structure, the fuzzy coefficients of each item in the structure were determined by the fuzzy regression approach. Finally, the mean relative error and mean systematic confidence of the fuzzy regression with nonlinear structure method were numerically calculated and compared with fuzzy least squares regression, fuzzy regression, adaptive neuro fuzzy inference system (ANFIS) and K-means-based ANFIS, and it was found that the proposed method was relatively more effective in modeling consumer preferences.
在在线销售平台中，产品设计属性影响消费者偏好，而消费者偏好也对未来产品设计的优化与迭代有着重大影响。在线评论数据是消费者对产品最直观的反馈。利用在线评论信息的价值来探索消费者偏好是优化产品、提高消费者满意度以及满足消费者需求的关键。因此，基于在线评论研究消费者偏好具有重要意义。然而，在以往基于在线评论的消费者偏好研究中，很少有研究对消费者偏好进行建模。这些模型常常存在非线性结构和模糊系数，使得构建明确的模型具有挑战性。因此，本研究采用具有非线性结构的模糊回归方法，基于在线评论对消费者偏好进行建模，为后续研究提供参考和见解。首先，选择智能手表作为研究对象，通过对产品在线数据进行文本挖掘，获取不同主题下产品评论的情感得分。其次，生成产品属性与消费者偏好之间的多项式结构，以进一步研究它们之间的关联。随后，基于现有的多项式结构，通过模糊回归方法确定结构中各项的模糊系数。最后，对具有非线性结构的模糊回归方法的平均相对误差和平均系统置信度进行数值计算，并与模糊最小二乘回归、模糊回归、自适应神经模糊推理系统（ANFIS）以及基于K均值的ANFIS进行比较，发现所提出的方法在对消费者偏好建模方面相对更有效。
