摘要： 由于微博文本的长度较短，直接使用隐狄利克雷分布（LDA）模型会导致特征向量高维稀疏。为此，提出一种融合标签语义的热点话题挖掘方法。利用公共块算法计算微博标签的相似度，合并标签相似度较高的微博文本。采用LDA模型对合并后的文本建模，并通过K-means聚类算法挖掘微博热点话题。实验结果表明，与针对单一微博文本建模的方法以及直接合并相同标签的方法相比，该方法的困惑度较低，挖掘热点话题的准确性较高。

关键词: 微博文本, 隐狄利克雷分布模型, 标签语义, 公共块, K-means聚类

Abstract: Due to the short length of the microblog text,the direct use of Latent Dirichlet Allocation(LDA) model will lead to high-dimensional sparse feature vectors.Thus,a hot topic mining method integrating tag semantics is proposed.The common block algorithm is used to calculate the similarity of the microblog tags,and the microblog texts with high tag similarity are combined.The merged text is modeled by LDA model,and the hot topic of microblog is mined by K-means clustering algorithm.Experimental results show that compared with the method of modeling a single microblog text and the method of directly merging the same label,the proposed method obtains a lower perplexity and a higher accuracy in mining topics.

Key words: microblog text, Latent Dirichlet Allocation(LDA) model, tag semantics, common block, K-means clustering

中图分类号: 

• TP393