基于联合分类器过滤噪声的微博主题发现

摘要

With the popularity of the Internet, microblogging as a representative of the social network has generated a lot of data. Exploring useful information from these data has become an important direction for today's research. According to the characteristics of microblogging text, this paper presents a method based on joint classifier to filter out noise microblogging, and then uses LDA model for subject discovery. The joint classifier model is composed of naive Bayesian, support vector machine and decision tree. The accuracy of the combined classifier is 87％, which can clearly show that this classification method is feasible and effective.%伴随着互联网的广泛流行,以微博为代表的社交网络产生了大量的数据.从这些数据中挖掘到有用的信息成为当今研究的一项重要方向.根据微博文本的特点,本文提出来一种基于联合分类器过滤掉噪声微博,然后利用LDA模型进行主题发现.联合分类器模型是由朴素贝叶斯、支持向量机和决策树三种模型通过简单投票机制结合构成的,实验结果联合分类器的准确度达到87％,显然这种分类方法是可行的,也是有效的.