A Status Property Classifier of Social Media User's Personality for Customer-Oriented Intelligent Marketing Systems

Tsung-Yi Chen, Yuh-Min Chen, Meng-Che Tsai
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引用次数: 1

Abstract

Enterprises need to obtain information about not only specific customer preferences, but also, more importantly, customers' psychological characteristics that significantly influence their consumption behaviors and response to intelligent-based marketing activities. If enterprises want to implement more precise intelligent selling activities for customers, customers' personality information will serve as a highly valued reference. The automatic detection method proposed in this study is based on techniques such as text semantic mining and machine learning to conduct personality type prediction on the target by collecting and analyzing the target's social media data. In the test, 5,858 statuses were obtained, 815 of which were labeled, with 122 effective tags. In general, when n = 5, the labeling rate can reach 60-80%. The status property classifier (SPC) proposed in this study can predict the personality type (PT) of the user publishing the status set with a high degree of accuracy by conducting text semantic mining on the status set.
面向客户的智能营销系统中社交媒体用户个性的状态属性分类
企业不仅需要了解顾客的具体偏好,更重要的是需要了解顾客的心理特征,这些心理特征会显著影响顾客的消费行为和对智能化营销活动的反应。如果企业想要为客户实施更精准的智能销售活动,客户的个性信息将是一个非常有价值的参考。本研究提出的自动检测方法是基于文本语义挖掘和机器学习等技术,通过收集和分析目标的社交媒体数据,对目标进行人格类型预测。测试共获得5858个状态,其中815个状态被标记,有效标签122个。一般情况下,当n = 5时,标注率可达60-80%。本文提出的状态属性分类器(SPC)通过对状态集进行文本语义挖掘,能够以较高的准确率预测发布状态集的用户的人格类型(PT)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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