基于K-means与Word2vec的哺乳文胸评论主题挖掘研究
Research on Topic Mining of Breastfeeding Bra Comments Based on K-means and Word2vec
刘妍 1刘驰1
作者信息
- 1. 西安工程大学 服装与艺术设计学院,西安 710048
- 折叠
摘要
目的 为了了解消费者在网络平台购买哺乳文胸时的关注侧重点,文章从在线评论中抽取有效关键词构建哺乳文胸主题,并通过计算主题的重要程度协助商家了解消费者关注重点方向.方法 选用TF-IDF关键词抽取算法,结合K-means和Word2vec进行语义聚类、主题识别、主题词挖掘及主题重要度计算.结果 哺乳文胸评论文本聚类后的主题重要程度排名是:产品品质(45.47%)、产品外观(35.83%)、产品服务(18.79%).结论 通过该方法能够有效的识别和构建哺乳文胸主题及主题词,同时,通过主题的重要程度,能够了解消费者对于网络平台购买哺乳文胸时关注的重点方向,为哺乳内衣企业进行产品改善及生产等提供理论参考.
Abstract
Objective In order to understand the focus of consumers when purchasing breastfeeding bras on on-line platforms,the article constructs breastfeeding bra themes by extracting effective keywords from online reviews,and assists merchants in understanding the direction of consumers'focus by calculating the importance of the themes.Methods The TF-IDF keyword extraction algorithm is selected,combined with K-means and Word2vec for se-mantic clustering,topic identification,topic word mining and topic importance calculation.Results The results showed that the ranking of topic importance after clustering breast feeding bra review texts was:product quality(45.47%),product appearance(35.83%),and product service(18.79%).Conclusion The results confirm that the method can effectively identify and construct the breastfeeding bra theme and theme words,and at the same time,through the importance of the theme,it can understand the key direction of consumers'concern for the purchase of breastfeeding bra on the online platform,which provides theoretical references for breastfeeding underwear enterprises to improve their products and production and soon.
关键词
服装工程/文本聚类分析/哺乳文胸/在线评论/K-means/Word2vec/主题挖掘/主题重要程度/文献计量分析Key words
clothing engineering/text-clustering analysis/breastfeeding bra/online comments/K-means/Word2vec/theme mining/theme importance/bibliometric analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据