Analysis Of Semantic Fuzziness Of Chinese Emotional Words And Its Application In Opinion Mining

In recent years,the rapid development of information technology has promoted the development of e-commerce trade and the online interactive platform,and also stimulated the proliferation of online comment text.Through the research of a large number of network reviews,it can help the government,business and customers to make more reasonable and more favorable decisions.As the importance of network comment has been valued by more and more people,the analysis of the emotion tendentiousness of the network comment text has become one of the research hot-spots in the field of natural language processing.In addition,by carrying out quantitative analysis on the polarity intensity of the emotional words,the emotional color degree of the emotional words can be effectively distinguished,thereby helping people to carry out more accurate emotional expression.Therefore,the research on the semantic fuzziness of emotional words is also a research hot-spot.This paper selects the emotional words in the online review as the research object,and combines the characteristics of the Chinese language to quantify the semantic fuzziness of the emotional words,and applies them in the field of opinion mining.The main work of this paper is:1.Based on How Net emotion dictionary,a dictionary construction method based on Word2 vec word vector and How Net semantic similarity linear superposition is proposed.The How Net emotion dictionary is expanded within the scope of NTUSD emotion dictionary and network emotion dictionary.2.Aiming at the semantic fuzziness of simple emotional words,a new method combining Word2 vec and How Net linear superposition and word frequency statistics is proposed to quantify the polar intensity of simple emotional words.Experiments show that this method can effectively improve the accuracy of polarity quantification of emotional words by quantifying the semantic fuzziness of emotional words.Compared with Ku et al's method,the accuracy of polarity intensity quantification increased by 10.8 percentage points when ? = 0.3.3.According to the emotional structure of complex structure,the corresponding emotional polarity intensity quantification method is designed according to the different structural characteristics of each small category,and then the proposed method is experimentally verified.According to the experimental results,it is found that the correct rate of polarity intensity quantification by this method is increasing by 13.5 percentage points when? = 0.3.4.In the opinion mining to carry on the concrete application to the above proposedmethod.First of all,a comparative experiment on the experimental text set with or without fusion emotion dictionary is carried out on the classification model.The experimental results show that the opinion mining with the fusion emotion dictionary has higher accuracy.The accuracy of the four classification models in positive text and negative text is increased by5.1% and 5.1%,2.8% and 3.4%,6% and 6.6%,3.4% and 2.7%.Secondly,the traditional NB,SVM,CNN and RNN classification algorithms are improved,and the contrastive experiments of the fusion sentiment dictionary are carried out on the improved classification model.The experimental results show that the accuracy of the four improved classification models in the positive text and negative text is increased by 1.7% and 4.2%,2.9% and 3.4%,2.2% and 2.3%,3.3% and 3.5%.