摘要
Web2.0环境下,越来越多的消费者在网络平台上购买商品,且将使用感受通过在线评论的形式表现出来,大量的在线评论数据蕴含着很多有价值的信息,企业可以利用在线评论来识别和分析用户需求,以便于后续的产品改进。本文以联想笔记本电脑的评论数据为研究对象,提出基于在线评论挖掘的用户需求识别与演化分析模型,利用SnowNLP模型、Kano模型与LDA模型,对用户评论进行分类、识别、特征情感对分析以及时间序列分析。结果表明:根据情感趋势预测,顾客对类型一、类型二和类型三的情感值呈上升趋势,类型四的情感值呈下降趋势;此外,用户对产品外观与游戏体验的关注较多。研究从时间的角度对在线评论的研究方法和模型进行了改进,可为分析用户对产品需求以及预测用户对于产品的情感趋势等研究提供参考价值。
In the Web2.0 environment,more and more consumers are purchasing products on online platforms and expressing their feelings through online reviews.A large amount of online review data contains valuable information,and enterprises can use online reviews to identify and analyze user requirements for subsequent product improvement.This article takes review data from Lenovo laptops as the research object and proposes a user requirements identification and evolution analysis model based on online reviews mining.The SnowNLP model,Kano model,and LDA model are used to classify,identify,analyze feature sentiment pairs,and analyze time series of user reviews.The results show that according to the sentimental trend prediction,customers′sentiment values for type 1,type 2,and type 3 show an upward trend,while the sentiment values for type 4 show a downward trend;In addition,users pay more attention to the appearance of the product and the gaming experience.The research has improved the research methods and models of online reviews from a time perspective,providing reference value for analyzing user requirements for products and predicting user sentimental trends towards products.
作者
王克勤
高智姣
乔亚楠
李靖
同淑荣
WANG Keqin;GAO Zhijiao;QIAO Yanan;LI Jing;TONG Shurong(School of Management,Northwestern Polytechnical University,Xi′an 710072,China)
出处
《机械科学与技术》
CSCD
北大核心
2023年第7期1070-1080,共11页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金青年项目(72101204)
陕西省自然科学基金项目(2022JM-421)。
