Machine Learning Methods for Content - Classification and Categorization

Authors

  • Prabhat Kubal  Student, Department of Computer Science, S.K.Somaiya College, Somaiya Vidyavihar University, Mumbai, India
  • Prof. Surabhi Thorat  Professor, Department of Computer Science, S.K.Somaiya College, Somaiya Vidyavihar University, Mumbai, India
  • Prof. Swati Maurya  Professor, Department of Computer Science, S.K.Somaiya College, Somaiya Vidyavihar University, Mumbai, India

DOI:

https://doi.org/10.32628/CSEIT217648

Keywords:

Text Detection, Text Analysis, Content Analysis, Social Media, Education, Toxic Comment Classification

Abstract

These days online gatherings and web-based media stages have furnished people with the necessary resources to advance their contemplations and put themselves out there free paying little heed to the kind of language used to communicate those thoughts, in certain examples these internet based remarks contain express language which might hurt the peruser. We likewise evaluate the class irregularity issues related with the dataset by utilizing inspecting procedures and misfortune. Models we applied yield high in general exactness with moderately minimal expense. To diminish the adverse consequence of poisonous remark in everyday life we have endeavored to plan a Toxic Language detector.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-12-30

Issue

Volume 7, Issue 6, November-December-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prabhat Kubal, Prof. Surabhi Thorat, Prof. Swati Maurya, " Machine Learning Methods for Content - Classification and Categorization" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 6, pp.184-189, November-December-2021. Available at doi : https://doi.org/10.32628/CSEIT217648