结合互信息和主题模型的微博话题发现方法

Microblog hot topic detection based on positive point mutual information and probabilistic topic model

为了解决短文本信息流的特征稀疏性对热点话题发现带来的挑战,提出了结合词语互信息和概率主题模型的微博热点话题发现方法。通过建立词共现矩阵并应用对称非负矩阵分解算法获取词项-主题矩阵,再利用概率潜在语义分析模型进行主题发现,最终通过定义微博热度分析和排序,有效地支持微博热点话题发现。实验表明,此方法能有效地进行话题聚类并检测出热点话题。

In order to face the challenges of feature sparsely of short text messages for microblog hot topic detection, this paper proposes a hot topic detection method based on the combination of term mutual information and probabilistic topic model. Symmetric Nonnegative Matrix Factorization（s NMF）is performed on word co-occurrence with word mutual information and the matrix of term-topic matrix is thereafter inferred. Probabilistic Latent Semantic Analysis（p LSA）model is then adopted to model the topic-microblog. The hotness of topic is analyzed and sorted. Experiments show that this method can effectively cluster and detect the hot topics.