CAJ | 학술논문

以微博为代表的社交网络已成为社会舆情的战略要地。对于社交网络中隐含主题社区的发掘，具有较高的商业推广和舆情监控价值。近年来，概率生成主题模型LDA（ Latent Dirichlet Allocation ）在数据挖掘领域得到了广泛应用。但是，一般而言，LDA适用于处理文本、数字信号数据，并不能合理地用来处理社交网络用户的关系数据。对LDA进行修改，提出适用于处理用户关系数据的Tri-LDA模型，挖掘社交网络中的主题社区。实验结果表明，基于Tri-LDA模型，进行机器学习所得到的结果基本能够反映社交网络上真实的主题社区分布情况。
이미박위대표적사교망락이성위사회여정적전략요지。대우사교망락중은함주제사구적발굴，구유교고적상업추엄화여정감공개치。근년래，개솔생성주제모형LDA（ Latent Dirichlet Allocation ）재수거알굴영역득도료엄범응용。단시，일반이언，LDA괄용우처리문본、수자신호수거，병불능합리지용래처리사교망락용호적관계수거。대LDA진행수개，제출괄용우처리용호관계수거적Tri-LDA모형，알굴사교망락중적주제사구。실험결과표명，기우Tri-LDA모형，진행궤기학습소득도적결과기본능구반영사교망락상진실적주제사구분포정황。
Social networks has gained huge popularity in particular microblogs in recent years .The discovery of latent topic com-munities in social networks carries high value in commercial promotion , public opinion monitoring , etc.In recent years , probabi-listic generative topic model (Latent Dirichlet Allocation, LDA) has been widely applied in the field of data mining .Generally, LDA can process text or digital signal data , however , without any modification , it lacks the capability to properly process the rela-tion data between users in a social network .By modifying the original LDA model , this essay proposes a new model , Tri-LDA and applies it to dig the hidden topic communities in a social network .The experiment result shows that the topic communities found by Tri-LDA is basically consistent with the realistic topic communities that hand-labeled by the authors .