CAJ | 학술논문

随着微博的日趋流行，新浪微博已成为公众获取和传播信息的重要平台之一，针对微博数据的话题挖掘也成为当前的研究热点。提出一个面向大规模微博数据的话题挖掘方法。首先对大规模微博数据进行分析，基于Bloom Filter算法对数据进行去重处理，针对微博的特有结构，对文本进行预处理，提出改进的LDA主题模型So-cial Network LDA（SNLDA），采用吉布斯采样法进行模型推导，挖掘出微博话题。实验结果表明，方法能有效地从大规模微博数据中挖掘出话题信息。
수착미박적일추류행，신랑미박이성위공음획취화전파신식적중요평태지일，침대미박수거적화제알굴야성위당전적연구열점。제출일개면향대규모미박수거적화제알굴방법。수선대대규모미박수거진행분석，기우Bloom Filter산법대수거진행거중처리，침대미박적특유결구，대문본진행예처리，제출개진적LDA주제모형So-cial Network LDA（SNLDA），채용길포사채양법진행모형추도，알굴출미박화제。실험결과표명，방법능유효지종대규모미박수거중알굴출화제신식。
With the daily popularity of microblog, Sina Weibo has become one of the important public access to and dis-semination of information platform, microblog topic mining has become a current research focuses. This paper proposes a topic mining method on massive Social Network data. This paper analyzes the large-scale microblog data, uses Bloom Filter algorithm to eliminate the duplicate data. In view of the special structure of microblog, filter the text. SNLDA, an improved LDA topic model is proposed in this paper, Gibbs sampling is chosen to deduce the model, which can mine the microblog topics. The experimental results show that the method can effectively excavate the topics from the large-scale microblog data.