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

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Abstract

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.

Keywords

ESA Wikipedia Micro-blog Sentiment analysis Naive Bayesian model 

Notes

Acknowledgements

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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Copyright information

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

Authors and Affiliations

  1. 1.School of AnimationHuanghuai UniversityZhumadianChina

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