Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 446-450.doi: 10.11896/j.issn.1002-137X.2017.6A.100

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Research on Chinese Texts Sentiment Classification Approach Based on PSO-GP

HUANG Yi and WANG Juan   

  • Online:2017-12-01 Published:2018-12-01

Abstract: The Chinese texts sentiment orientation analysis is one of the key technologies for network public opinion information mining and analysis.This paper proposed a Chinese texts sentiment classification method based on particle swarm optimization-Gaussian process (PSO-GP) algorithm,which employs optimal hyper parameter search.It solves problems of traditional Gauss iteration process,like difficultly to determine conjugated gradient,strongly dependent on initial value and easily to fall into local minimum.It can collect data set text corpus to construct a domain specific emotion dictionary using multi-threaded web crawler technology,select the most effective features by emotional words,reduce the data dimension,and generate feature vector from feature words by TF-IDF algorithm.Experimental results show that the classification accuracy of the improved classification model is improved by nearly 15%.

Key words: Chinese texts sentiment classification,Web crawlers,Semantic lexicon,Particle swarm optimization,Gaussianprocess

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