Sentiment Analysis of Micro-blog Integrated on Explicit Semantic Analysis Method



Combined with existing research of short text classification, this paper analyzes and explores the structure and characteristics of Wikipedia. And the fusion of explicit semantic analysis algorithm micro-blog sentiment analysis method is proposed. Wikipedia is regarded as external semantic knowledge base, and the entries are introduced as a supplement of micro-blog text features. The author improves the previous micro-blog sentiment analysis text representation method, and then constructs the naive Bias classifier to achieve emotion classification. The experimental results show that after the introduction of Wikipedia to micro-blog text feature expansion, the final evaluation index of the classification results of the naive Bias classifier has been improved, which achieves a better classification effect and improves the effectiveness of the sentiment classification.


ESA Wikipedia Micro-blog Sentiment analysis Naive Bayesian model 



The authors acknowledge the National Natural Science Foundation of China (Grant: 111578109), the National Natural Science Foundation of China (Grant: 11111121005). Funding was provided by Henan science and technology plan project (Grant No. 172102210117).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AnimationHuanghuai UniversityZhumadianChina

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