| 腾讯微博用户的特征分析 |
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| 引用本文： | 杨小朋,何跃.腾讯微博用户的特征分析[J].情报杂志,2012(3):84-87. |
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| 作者姓名： | 杨小朋 何跃 |
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| 作者单位： | 四川大学工商管理学院 |
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| 摘 要： | 论文采集腾讯微博数据,提出"博文魅力指数"的概念,并运用Spearman和Pearson相关系数分别对听众数与收录博主人数、博文魅力指数与收录博主人数两对变量进行了相关分析,最后选择博文魅力指数,博主收听人数两个变量使用K-Means聚类算法对微博用户进行了聚类分析。研究结果表明:博文魅力指数与收录博主人数两变量中度正相关;聚类将微博用户分为信息获取型、草根名人型和普通社交型三类。电子商务服务商可以通过算法优化,根据博文魅力指数和详细的聚类结果更有针对性的进行页面和应用程序推荐,创造商业价值。
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| 关 键 词： | 微博 博文魅力指数 Pearson相关系数 K-Means聚类 |
Analysis on the User’s Data of Tencent Micro-blog |
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| YANG Xiaopeng,HE Yue.Analysis on the User’s Data of Tencent Micro-blog[J].Journal of Information,2012(3):84-87. |
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| Authors: | YANG Xiaopeng HE Yue |
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| Institution: | (Business School,Sichuan University,Chengdu 610064) |
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| Abstract: | Based on the data collected of Tencent Micro-blog,this paper proposes the concept of "the content’s charm index of micro-blog",then uses Pearson and Spearman correlation coefficient to analyze the relationship between the number of listeners and the number of users,and the relationship between "the content’s charm index of micro-blog" and the number of users.At last,the paper uses K-Means clustering algorithm to analyze the characteristics of users.The conclusion is that the content’s charm index of micro-blog has moderate positive correlation with the number of users.K-Means clustering classifies the users into three types such as information-obtaining type,grassroots celebrities and ordinary social networking type.Thus,by optimizing the algorithm and using the content’s charm index of micro-blog and the result of clustering,service providers can reduce unnecessary recommendations of pages and application to meet the needs of users so as to acquire commercial value. |
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| Keywords: | micro-blog the content’s charm index of micro-blog Pearson correlation coefficient K-Means clustering |
| 本文献已被 CNKI 等数据库收录！ |
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