Advertisement

Public Sentiment Monitoring and Early-Warning for Enterprise

  • Zhen QiuEmail author
  • Di Liu
  • Qiyuan Wang
  • Yingbao Cui
  • Xusheng Yang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

With the increasing global competitiveness and influence, a lot of famous central government-owned companies has taken a massive amount of attention worldwide. Meanwhile, the increasing complex internal and external environment has also bring serious management risk, particularly in terms of public opinion. The public opinion crisis may cause incalculable impact and damage to those companies brand management. Thus, it is significantly to accurately collect public opinion on company major policy, business and product. Then, in order to timely maintenance positive management environment, the company should pay attention to positioning source of public opinion, carrying out public opinion treatment in time. With the rapid development of AI (artificial intelligence) technology, it has become the focus of the new generation information technology. Based on the artificial intelligence technology, this paper realized “topic model”, “position determination” and “public opinion tracing” modules in public opinion monitoring and risk early warning. All those achievements of this paper can be applied to company management, brand maintenance, network public opinion analysis.

Keywords

Management Public sentiment Artificial intelligence 

References

  1. 1.
    Jin, Y., Qi, M., Goodison, S., Mai, V., Sun, Y.: Feature selection for unsupervised learning through local learning. Pattern Recogn. Lett. 53, 100–107 (2015)CrossRefGoogle Scholar
  2. 2.
    Fang, A., Macdonald, C., Ounis, I., Habel, P., Xiao, Y.: Exploring time-sensitive variational Bayesian inference LDA for social media data (2017)Google Scholar
  3. 3.
    Ji, Y., Eisenstein, J.: Entity-augmented distributional semantics for discourse relations. Comput. Sci. (2014)Google Scholar
  4. 4.
    Zhao, X., Evans, N., Dugelay, J.L.: Semi-supervised face recognition with LDA self-training. In: IEEE International Conference on Image Processing (2011)Google Scholar
  5. 5.
    Tang, J., Meng, Z., Nguyen, X.L., Mei, Q., Zhang, M.: Understanding the limiting factors of topic modeling via posterior contraction analysis. In: International Conference on International Conference on Machine Learning (2014)Google Scholar
  6. 6.
    Johnson, T.J., Zhang, W., Bichard, S.L.: Voices of convergence or conflict? A path analysis investigation of selective exposure to political websites. Soc. Sci. Comput. Rev. 29(4), 449–469 (2015) CrossRefGoogle Scholar
  7. 7.
    Somasundaran, S., Wiebe, J.: Recognizing stances in online debates. In: Joint Conference of the Meeting of the ACL the International Joint Conference on Natural Language Processing of the AFNLP (2010)Google Scholar
  8. 8.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Governance in social media: a case study of the Wikipedia promotion process (2010)Google Scholar
  9. 9.
    Chakrabarti, D., Faloutsos, C., Mcglohon, M.: Graph mining: laws and generators (2010)CrossRefGoogle Scholar
  10. 10.
    Gaoyan, O., Wei, C., Peng, L., Wang, T., Yang, D., Kai, L., Liu, Y.: Aspect-specific polarity-aware summarization of online reviews. Lecture Notes in Computer Science, vol. 7923, pp. 289–300 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhen Qiu
    • 1
    Email author
  • Di Liu
    • 1
  • Qiyuan Wang
    • 2
  • Yingbao Cui
    • 1
  • Xusheng Yang
    • 1
  1. 1.State Grid Information and Telecommunication GroupBeijingChina
  2. 2.State Grid Energy Conservation Design and Research InstituteBeijingChina

Personalised recommendations