摘要
分析了不同的主题模型,通过实验比较了3种主题模型构建的微博用户兴趣模型的性能。实验结果表明:TwitterLDA适用于新文档或新用户的预测,AuthorLDA产生的主题具有较高的区分度,而UserLDA和AuthorLDA能更好地反映出用户的社交网络关系。上述工作为进一步研究主题模型如何应用于微博的个性化信息推荐、情感分析和话题检测与跟踪等文本挖掘应用奠定了基础。
This paper analysed different topic models,and compared three extended topic models＇ performance on mo-deling microblog user interestingness via three experiments.Experimental results show that TwitterLDA can apply to predict words on new unseen docuemnts and users,that the topics generated by AuthorLDA have a higher degree of differentiation,and that UserLDA and AuthorLDA can better reflect the users＇ relationships in real social network.The work in this paper lays the foundation for further studying how the topic model is applied to the text mining applications of microblogs such as personalized recommendation,sentiment analysis and topic detection and tracking.
出处
《计算机科学》
CSCD
北大核心
2013年第4期127-130,135,共5页
Computer Science
基金
国家自然科学基金项目(61170189
60973105
61202239)
教育部博士点基金(20111102130003)资助
关键词
主题模型
用户兴趣
个性化服务
Topic model
User interest
Personalized service
