基于电商平台用户评价的需求分析

Demand Analysis Based on E-commerce Platform User Evaluation

电商平台的用户评价包含了用户对于产品的态度与关注点,为探究从中获取用户需求及关注度的可行性,使用Python网络爬虫从国内某电商平台爬取了学习台灯类产品的6348条评价数据,借助开源自然语言处理工具HANLP对数据进行分词、词性标注、关键词提取、词频分析、词云生成,提取出用户对于产品光学参数、交互方式、结构、产品外形等类别的需求点和关注度,并最终完成需求转化。结果表明:该方法应用于具有一定用户评价数据量的产品调研时,能显著加快需求分析速度;而对于数据量不足的产品,使用该方法会有一定的误差。

This study investigates the feasibility of extracting user demands and levels of concern from user evaluation on e-commerce platforms,which reflect users'attitudes and priorities towards products.A total of 6,348 user reviews for study desk lamps were collected from a prominent domestic e-commerce platform using a Python web crawler.The collected data were then processed using the open-source natural language processing(NLP)tool HANLP,including segmenta-tion,part-of-speech tagging,keyword extraction,word frequency analysis,and word cloud generation.By analyzing the data,user demands and levels of concern were identified in categories such as optical parameters,interaction methods,structure,and product appearance.These insights were further utilized to transform the identified demands into actionable outcomes.The results indicate that this method significantly accelerates the speed of demand analysis when applied to product research with a substantial amount of user review data.The extracted keywords provide valuable information on user preferences and priorities,enabling product improvement and design optimization.However,for products with limited rview data,the use of this method may be associated with a certain degree of error.