CAJ | 학술논문

随着微博的日趋流行与广泛使用，新浪等微博网站已经成为海量信息的来源，虽然传统的文本主题挖掘方法已经得到广泛的应用研究，但对于微博这种特殊结构的文本，传统的挖掘算法不能很好地对其进行研究。为了弥补目前微博平台主题挖掘方法的不足，以及考虑到微博信息的稀疏性，多维性等特点，提出有针对性的预处理方法，将用户微博数据与AT模型结合，通过吉布斯采样进行微博主题挖掘，对作者主题进一步提取得到用户兴趣。通过在真实数据集上的实验，以及与LDA模型对比，证明该模型能有效得到微博主题。
수착미박적일추류행여엄범사용，신랑등미박망참이경성위해량신식적래원，수연전통적문본주제알굴방법이경득도엄범적응용연구，단대우미박저충특수결구적문본，전통적알굴산법불능흔호지대기진행연구。위료미보목전미박평태주제알굴방법적불족，이급고필도미박신식적희소성，다유성등특점，제출유침대성적예처리방법，장용호미박수거여AT모형결합，통과길포사채양진행미박주제알굴，대작자주제진일보제취득도용호흥취。통과재진실수거집상적실험，이급여LDA모형대비，증명해모형능유효득도미박주제。
As micro-blog grows more popular and widely used, micro-blogging site such as Sina has become a huge source of information, although the traditional method of texts, topic mining has been extensively applied research. For this special kind of text of micro-blogging, traditional text mining algorithm can not be well studied. In order to compensate the deficiencies of current topic mining for micro-blogging platform and considering the sparsity and multidimensional characteristics of micro-blogging, this paper proposes targeted pretreatment method and combines the users’micro-blogging data with AT model, then mining the micro-blog topics by gibbs sampling, getting users’interest through extracting the topics of authors. Through the experiments on a real data sets, as well as comparison with LDA models prove that the model can get micro-blog topics effectively.