Research on Microblog Sentiment Analysis Based on Multi-feature
摘要
In order to improve the accuracy of micro-blog emotional classification recognition,regarding the network microblog data as research object,this paper proposes a choice method of emotional basic word based on graph.Combined with similarity knowledge of HowNet,the method builds a graph model to choose high quality and high coverage emotion basic words according to node betweenness centrality in graph.It builds emotional dictionary for sentiment analysis according to selected basic words.The polarity of emotional words is also given.The emotional words are applied to mine short sentence emotional features.Those features will join into traditional Support Vector Machine(SVM) model.More semantic information is mined on micro-blog sentences to obtain a more reasonable semantic composition function.The sentence emotional changes are captured to better grasp the micro-blog emotion of whole sentence.Conditional Random Field(CRF) model that has characteristics of feature constraint is used to classify short sentences.Experimental results verify the effectiveness of CRF model on short sentences.Compared with SVM classification methods with different features,it also has a greater effect on different data sets.