A machine learning methods for forecast prediction on social media users

Authors

  • Renu Kumari College of Commerce Arts and Science, Patliputra University, Patna, 800020 Bihar, India
  • Vijay Kumar College of Commerce Arts and Science, Patliputra University, Patna, 800020 Bihar, India

Keywords:

Machine Learning Approach, Vector Machine, Social media User

Abstract

The paper deals with “A machine learning methods for forecast prediction on social media users.” The networking data is a very important aspect in the present and future networking data plays a very valuable role in decision making for social media user, organization, education sector, and service. With the online existence and social media users utilize various social media platforms like Instagram/Facebook or others app to express or comments their observations and opinions. The article network is a unweighted and directed network. Article-author network is an unweighted, undirected, and two-mode network. Author network is an weighted and undirected network. Machine learning works by progressively improving the performance of a given task. Previously, machine learning provides us with powerful web search techniques, speech recognition, home automation, automated surveillance system, self-driving cars, and much-improved perception about the genome.

References

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Lee, M.J., Lee, J., Park, J.Y., Choi, R.H. and Chung, C.W., 2012, April. Qube: a quick algorithm for updating betweenness centrality. In Proceedings of the 21st international conference on World Wide Web (pp. 351-360).

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Published

2023-10-03

How to Cite

[1]
“A machine learning methods for forecast prediction on social media users”, JASRAE, vol. 20, no. 4, pp. 518–521, Oct. 2023, Accessed: Jun. 29, 2024. [Online]. Available: https://ignited.in/jasrae/article/view/14665

How to Cite

[1]
“A machine learning methods for forecast prediction on social media users”, JASRAE, vol. 20, no. 4, pp. 518–521, Oct. 2023, Accessed: Jun. 29, 2024. [Online]. Available: https://ignited.in/jasrae/article/view/14665