Text Classification using Data Mining and Machine Learning Techniques: A Brief Review

Authors

  • Diksha Kose  Department of Computer Engineering Bapurao Deshmukh College of Engineering Wardha, Maharashtra, India
  • Mayuri Harankhede  Department of Computer Engineering Bapurao Deshmukh College of Engineering Wardha, Maharashtra, India
  • Shivani Shukla  Department of Computer Engineering Bapurao Deshmukh College of Engineering Wardha, Maharashtra, India
  • Prof S.W.Mohod  Department of Computer Engineering Bapurao Deshmukh College of Engineering Wardha, Maharashtra, India

Keywords:

Text Classification, Machine Learning, Multiview Boosting, cMFDR, Visual Classifiers, Text Segmentation

Abstract

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-04-30

Issue

Volume 4, Issue 4, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Diksha Kose, Mayuri Harankhede, Shivani Shukla, Prof S.W.Mohod, " Text Classification using Data Mining and Machine Learning Techniques: A Brief Review, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 4, pp.356-360, March-April-2018.