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

Tsung-Yi Chen, Yuh-Min Chen, Meng-Che Tsai
{"title":"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","authors":"Tsung-Yi Chen, Yuh-Min Chen, Meng-Che Tsai","doi":"10.4018/978-1-7998-9020-1.ch029","DOIUrl":null,"url":null,"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.","PeriodicalId":302726,"journal":{"name":"Research Anthology on Strategies for Using Social Media as a Service and Tool in Business","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Status Property Classifier of Social Media User's Personality for Customer-Oriented Intelligent Marketing Systems\",\"authors\":\"Tsung-Yi Chen, Yuh-Min Chen, Meng-Che Tsai\",\"doi\":\"10.4018/978-1-7998-9020-1.ch029\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":302726,\"journal\":{\"name\":\"Research Anthology on Strategies for Using Social Media as a Service and Tool in Business\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research Anthology on Strategies for Using Social Media as a Service and Tool in Business\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-7998-9020-1.ch029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Anthology on Strategies for Using Social Media as a Service and Tool in Business","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-7998-9020-1.ch029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Status Property Classifier of Social Media User's Personality for Customer-Oriented Intelligent Marketing Systems
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Hiring the Best Job Applicants? An Evaluation of Toronto's Destination Image Through Tourist Generated Content on Twitter This Thing of Social Media! Top Museums on Instagram A Content Marketing Framework to Analyze Customer Engagement on Social Media
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1