摘要
在线评论作为用户偏好表达的主要途径之一,成为企业发掘顾客产品需求的主要数据源泉。本研究通过文本挖掘在线评论取代传统需求分析中的问卷调查的方法,采用机器学习算法提取有关产品需求的文本特征,再基于各特征属性的情感分析结果,使用改进后的IPA-KANO模型进行电商产品需求分类,提出产品提升策略。使用京东平台6个猫粮品牌的在线评论对本文提出的研究思路进行验证,结果表明,对于适口性、性价比等特征属性,企业应采取“继续保持”的策略;对于香味、品牌效应等特征属性,企业应采取“提升改进”策略。
As one of the main ways to express user preferences, online reviews have become the main data source for enterprises to explore customer needs. This study replaces the traditional questionnaire survey in demand analysis by text mining online review, and adopts machine learning algorithm to extract text features related to product demands. Then, based on the sentiment analysis results of each feature attribute, the improved IPA-KANO model is combined to classify e-commerce product demands and formulate product promotion strategies. The online reviews of 6 cat food brands on Jingdong platform were used to verify the research ideas proposed in this paper. The results showed that enterprises should adopt a “continue to maintain” strategy for features such as palatability and cost performance. For characteristic attributes such as fragrance and brand effect, enterprises should adopt the strategy of “upgrading and improving”.
出处
《电子商务评论》
2024年第1期211-222,共12页
E-Commerce Letters
