To quickly obtain information of theme and sentiment in network texts
the concept of sentiment summarization is introduced in order to meet the practical needs
and while a method of extracting the sentiment summarization is proposed based on the Conditional Random Fields model. Firstly
extract the basic features
such as sentence length
tips word and sentiment words in the sentence
while the Latent Dirichlet Allocation model is used to analyze the underlying themes of the text and extract theme features. Then these two kinds of features are used in the Conditional Random Fields model and sentiment summarization is obtained. Experimental results show that this approach has better efficiency because it takes into account the information of themes in the text