A Review on Text Classification Based on CNN

Authors

  • Sachin Sambhaji Patil   Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Anthon Rodrigues   Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Rahul Telangi   Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Vishwajeet Chavan   Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India

DOI:

https://doi.org//10.32628/IJSRST229677

Keywords:

NLP , CNN , DL , MLP

Abstract

Text can be an incredibly rich source of information, but extracting information from it can be difficult and time-consuming due to its unstructured nature. However, thanks to advances in natural language processing and machine learning, both of which are under the broad umbrella of artificial intelligence, it is getting easier and easier to organize textual data. It works by automating and structuring documents quickly and cost-effectively, so businesses can automate processes and uncover insights that help make good decisions. than. Instead of relying on manually generated rules, text classification using machine learning learns to perform classification based on past observations. By using pre-tagged examples as training data, machine learning algorithms can learn different associations between text fragments.

References

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International Journal of Scientific Research in Science and Technology

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Published

2022-12-30

Issue

Volume 9, Issue 6, November-December-2022

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

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

How to Cite

[1]
Sachin Sambhaji Patil, Anthon Rodrigues, Rahul Telangi, Vishwajeet Chavan, " A Review on Text Classification Based on CNN, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 6, pp.622-624, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRST229677