摘要
【目的】为有效解决微博客户特性的表示问题,以更好地实施企业微博客户细分。【方法】借助微博平台上客户的个人和社会关系特性,利用客户及其好友的自定义标签表示客户的特性,采用基于非负矩阵分解的文本聚类方法,提出一种面向企业微博的客户细分框架。【结果】实验结果表明,基于非负矩阵分解的方法取得约86.130%的asw指标平均值,远远超出基于K-means和层次聚类的方法。【局限】只通过融合微博客户个人及其关注好友的标签表示微博客户特性的方法不能够全面刻画客户特征。【结论】能够为企业微博客户细分中的客户特性的表示、细分、评价及结果可视化等问题提供参考和借鉴。
[Objective] This study tried to describe the customers’ characteristics effectively. [Methods] The proposed framework aimed to explore the personal and social relationship among the customers and their friends on the microblog platform. We described the customers’ characteristics using self-defined tags and then created segmentation with the help of text clustering and non-negative matrix factorization technologies. [Results] The method based on non-negative matrix factorization achieved an approximately 86.130% on average asw index, which outperformed traditional methods based on K-means and hierarchical clustering. [Limitations] The customers’ characteristic cannot be described only by himself and his friends with self-defined tags on Microblogging. [Conclusions] The proposed framework could improve the effectiveness of characteristics description, evaluation and visualization of microblog customer segmentation.
出处
《现代图书情报技术》
CSSCI
2016年第2期43-51,共9页
New Technology of Library and Information Service
基金
国家自然科学基金项目"面向微博公共事件的反向社会情绪识别及演化分析研究"(项目编号:61572145)
广东省科技计划项目"广东省企业竞争情报信息提取及态势推理机制研究--以汽车行业为例"(项目编号:2015A030401093)
广东大学生科技创新培育专项资金项目"微博用户生成内容挖掘及其在微博营销领域的应用研究"(项目编号:308-GK151019)的研究成果之一
关键词
客户细分
微博营销
文本聚类
非负矩阵分解
Customer segmentation
Microblogging marketing
Text clustering
Non-negative matrix factorization
