Cluster Computing

, Volume 22, Supplement 3, pp 6295–6304 | Cite as

Emotional component analysis and forecast public opinion on micro-blog posts based on maximum entropy model

  • Mingchuan ZhangEmail author
  • Ruijuan ZhengEmail author
  • Jing Chen
  • Junlong Zhu
  • Ruoshui Liu
  • Shibao Sun
  • Qingtao Wu


As the main carrier and platform of spreading network public opinion, micro-blog makes information disseminate more quickly and the influence of public opinion increased. Therefore, accurate analysis and prediction of micro-blog emotion are of great significance for predicting and controlling public opinion. In this paper, we propose the emotional component analysis and public opinion forecast on Chinese micro-blog posts based on maximum entropy model, which uses fine-grained to classify emotion of Chinese micro-blog. Firstly, we preprocess the Chinese micro-blog to filter the noise data. Moreover, the document frequency method and information gain principle are combined to extract features. Secondly, the maximum entropy model is employed to train classifier, and the selective integrated classifiers are used to analyze emotion. On this basis, the principle of the minority subordinate to the majority is used to predict public opinion. In addition, the experimental results have shown the accuracy of the proposed algorithm is 0.88, and the comparison of the four indicators of accuracy, recall, F-Measure and convergence error verify the feasibility and effectiveness of the proposed method.


Micro-blog Information gain principle Maximum entropy Emotional classification Public opinion forecast 



This work is partially supported by the National Natural Science Foundation of China (NSFC) under Grants Nos. 61602155, 61370221 and U1604155, in part by the Program for Science & Technology Innovation Talents in the University of Henan Province under Grants No. 16HASTIT035, and in part by Henan Science and Technology Innovation Project under Grant Nos. 164200510007 and 174100510010, and in part by the Industry university research project of Henan Province under Grant No. 172107000005, and in part by the Program for Innovative Research Team in University of Henan Province under Grants No. 17IRTSTHN010, and in part by the support program for young backbone teachers in Henan Province under Grant no. 2015GGJS-047.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.


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

Authors and Affiliations

  1. 1.Information Engineering CollegeHenan University of Science and TechnologyLuoyangChina

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