An Analysis of Emotional Tendency Under the Network Public Opinion: Deep Learning

Authors

  • Jinze Li
  • Yizhen Wang
  • Jun Wang

DOI:

https://doi.org/10.31449/inf.v45i1.3402

Abstract

Network public opinion refers to the common opinion with tendency and influence formed by the public on certain social events through the Internet. Due to the complexity of interest relations, network public opinion is likely to cause difficulties for individuals, enterprises or governments. In order to control the public's emotional tendency to social events, this study designed an OCC sentiment rule system to label the network public opinion case base. The text representation method is Word2Vec in deep learning, and the convolution neural network is used to construct the sentiment tendency analysis model under the network public opinion. Taking the case of Dujia Banna humiliation incident, Xiangshui explosion incident and baixiangguo girl's murder as the research cases, the accuracy of the model to identify the above three events was 85.87%, 73.65% and 85.87% respectively under the optimal parameters setting. The experimental results show that compared with the manual annotation method, the proposed method can improve the accuracy of emotion recognition by 3.00% ~ 8.00%. This shows that the network public opinion sentiment orientation recognition model constructed in this study has a high recognition accuracy, and can be used to assist relevant departments to detect network public opinion.

Downloads

Published

2021-03-15

How to Cite

Li, J., Wang, Y., & Wang, J. (2021). An Analysis of Emotional Tendency Under the Network Public Opinion: Deep Learning. Informatica, 45(1). https://doi.org/10.31449/inf.v45i1.3402

Issue

Vol. 45 No. 1 (2021)

Section

Student papers

License

I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.

I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.

In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.

I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal.  The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.


Copyright ©  Slovenian Society Informatika