基于狄利克雷多项分配模型的多源文本主题挖掘模型

随着文本数据来源渠道越来越丰富，面向多源文本数据进行主题挖掘已成为文本挖掘领域的研究重点。由于传统主题模型主要面向单源文本数据建模，直接应用于多源文本数据有较多的限制。针对该问题提出了基于狄利克雷多项分配（DMA）模型的多源文本主题挖掘模型——多源狄利克雷多项分配模型（MSDMA）。通过考虑主题在不同数据源的词分布的差异性，结合DMA模型的非参聚类性质，模型主要解决了如下三个问题：1）能够学习出同一个主题在不同数据源中特有的词分布形式；2）通过数据源之间共享主题空间和词项空间，使得数据源间可进行主题知识互补，提升对高噪声、低信息量的数据源的主题发现效果；3）能自主学习出每个数据源内的主题数量，不需要事先给定主题个数。最后通过在模拟数据集和真实数据集的实验结果表明，所提模型比传统主题模型能更有效地对多源数据进行主题信息挖掘。

Multi-source text topic mining model based on Dirichlet multinomial allocation model

With the rapid increase of text data sources, topic mining for multi-source text data becomes the research focus of text mining. Since the traditional topic model is mainly oriented to single-source, there are many limitations to directly apply to multi-source. Therefore, a topic model for multi-source based on Dirichlet Multinomial Allocation model (DMA) was proposed considering the difference between sources of topic word-distribution and the nonparametric clustering quality of DMA, namely MSDMA (Multi-Source Dirichlet Multinomial Allocation). The main contributions of the proposed model are as follows:1) it takes into account the characteristics of each source itself when modeling the topic, and can learn the source-specific word distributions of topic k; 2) it can improve the topic discovery performance of high noise and low information through knowledge sharing; 3) it can automatically learn the number of topics within each source without the need for human pre-given. The experimental results in the simulated data set and two real datasets indicate that the proposed model can extract topic information more effectively and efficiently than the state-of-the-art topic models.