Modeling Social Networks using Data Mining Approaches-Review

Authors

  • Fatima Hassan Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq
  • Suhad Faisal Behadili Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2022.63.3.35%20

Keywords:

DataMining, Analysis of Networks of Social Media, Community Analysis, Sentiment, Opinion

Abstract

     Getting knowledge from raw data has delivered beneficial information in several domains. The prevalent utilizing of social media produced extraordinary quantities of social information. Simply, social media delivers an available podium for employers for sharing information. Data Mining has ability to present applicable designs that can be useful for employers, commercial, and customers. Data of social media are strident, massive, formless, and dynamic in the natural case, so modern encounters grow. Investigation methods of data mining utilized via social networks is the purpose of the study, accepting investigation plans on the basis of criteria, and by selecting a number of papers to serve as the foundation for this article. Afterward a watchful evaluation of these papers, it has beeniscovered that numerous data extraction approaches were utilized with social media data to report a number of various research goals in several fields of industrial and service. Though, implementations of data mining are still raw and require more work via industry and academic world to prepare the work sufficiently. Bring this analysis to a close. Data mining is the most important rule for uncovering hidden data in large datasets, especially in social network analysis, and it demonstrates the most important social media technology.

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Published

2022-03-30

How to Cite

Hassan, F. ., & Behadili, S. F. . (2022). Modeling Social Networks using Data Mining Approaches-Review. Iraqi Journal of Science, 63(3), 1313–1338. https://doi.org/10.24996/ijs.2022.63.3.35

Issue

Section

Computer Science