摘要
【目的/意义】为了协助商家和平台获取移动商务在线评论中的用户需求,解决在线评论过载导致用户需求提取困难等问题。【方法/过程】本文首先获取原始在线评论数据集进行文本预处理和清洗;然后,深入语义层面基于改进后的Canopy-Kmeans算法实现用户需求聚合;最后,以聚合结果为层级指标设计Kano问卷,用重要性判别方法和用户满意度指数优化用户需求分类标准,实现用户需求的高效聚合和精准挖掘。【结果/结论】通过实验结果对比分析发现与基于语义的传统聚类方法相比,本文设计的移动商务用户需求聚合与挖掘方法的聚类结果更清晰合理,能够获取更精准和细化的用户需求。【创新/局限】借助Word2vec模型从语义的视角分析用户需求,提出基于Canopy-Kmeans算法的用户需求聚合挖掘模型,但选取的研究对象和数据规模较为有限,下一步将扩大在线商品评论的研究范围及实验数据规模。
【Purpose/significance】This study is to help businesses and platforms obtain user requirements in mobile commerce online reviews,and solve the problem of user requirements extraction caused by online review overload.【Method/process】Firstly,this paper obtains the original online review data set for text preprocessing and cleaning.Then,it goes deep into semantic level to achieve user needs aggregation based on the improved Canopy-Kmeans algorithm;finally,the Kano questionnaire is designed with the aggregation results as the hierarchical index,and the classification standard of user needs is optimized with the importance discrimination method and the user satisfaction index to achieve the efficient aggregation and accurate mining of user needs.【Result/conclusion】Through the comparative analysis of the experimental results,it is found that compared with the traditional semantic-based clustering method,the clustering results of the mobile commerce user demand aggregation and mining method designed in this paper are more clear and reasonable,and can obtain more accurate and refined user needs.【Innovation/limitation】The Word2Vec model is used to analyze user needs from a semantic perspective,and the optimized Canopy-Kmeans algorithm is used to aggregate user needs,but the selected research objects and data size are relatively limited,then the research scope and experimental data size of online product comments will be expanded in the future.
作者
吴运明
王令村
魏子栋
郭顺利
WU Yun-ming;WANG Ling-cun;WEI Zi-dong;GUO Shun-li(College of Communication,Qufu Normal University,Rizhao 276826,China)
出处
《情报科学》
CSSCI
北大核心
2022年第10期97-106,共10页
Information Science
基金
山东省自然科学基金青年项目“大数据驱动下移动消费者在线短评论隐式意见挖掘及网络口碑预警研究”(ZR2020QG024)
作者简介
吴运明(1980-),男,山东临沂人,博士研究生,讲师,主要从事教育信息化、文本挖掘与分析相关研究;王令村(2001-),女,山东潍坊人,本科生,主要从事文本挖掘与分析、商务智能相关研究;魏子栋(2001-),男,山东临沂人,本科生,主要从事教育信息化,文本挖掘与分析研究;郭顺利(1989-),男,山东临沂人,博士,副教授,主要从事知识组织与管理、商务智能相关研究。
