基于CTM模型的观点挖掘和可视化

如何从海量文本中自动提取有价值的观点信息已成为重要的技术挑战,当下的观点挖掘方法大多数是在假设主题相互独立的前提下进行的,但实际上主题与主题之间有着复杂的内在联系。为解决以上问题,在CTM模型的基础上提出了基于主题情感混合的主题相关模型,在考虑到主题相关性的同时,还分析了对应主题下的观点特征以及潜藏的情感倾向,更为精确地获取了文档主题的情感极性,仿真实验验证了本模型的有效性,并使用R语言进行了可视化实验分析。

Opinion mining and visualization based on CTM model

How to automatically extract valuable opinion information from enormous texts has become an important technical challenge. Currently, most opinion mining methods are based on the assumption that topics are independent of each other. However, there are complicated inherent relationships between topics. In order to solve the above problems, based on standard CTM model, the paper proposes a hybrid correlated topic model that mixes topic with sentiment to perform opinion mining. Considering the topic correlation of documents, opinion characters and potential sentiment tendency are analyzed. Based on these results, sentiment polarity of the whole review and each topic are obtained. The simulation results verify the validity of the proposed model. R language is also used to visualize the experimental results.