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Exploring the Use of Machine Learning Algorithms in Text Classification

  • Preeti Naval
  • , Krishnan Batri
  • , D. Nirmala
  • , Pawan S. Wawage
  • , Narendra Kumar Jain
  • , Jatin Khurana

Research output: Chapter in Book/ReportConference contributionpeer-review

Abstract

The use of device mastering algorithms has become increasingly popular in text type. The essential benefits of using these algorithms are their ability to perceive trends in text statistics and examine the information they may be uncovered. Textual content classification entails sorting textual content into predefined categories or lessons using diverse algorithms. Specific device studying strategies, including assist vector machines (SVMs) and artificial neural networks (ANNs), have been carried out for textual content class duties. Those algorithms help identify the applicable topics in extensive text collections and allow the categorization of files into nicely-defined topic clusters. This paper explores the software of machine studying algorithms in text category obligations. Further, a number of the popular algorithms and their applications on this subject and their blessings and limitations are discussed. Sooner or later, conclusions and destiny paintings are discussed.

Original languageAmerican English
Title of host publication2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350370249
DOIs
StateIndexed - 2024
Externally publishedYes
Event15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024 - Kamand, India
Duration: 24 Jun 202428 Jun 2024

Publication series

Name2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

Conference

Conference15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
Country/TerritoryIndia
CityKamand
Period24/06/2428/06/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Applications
  • Categorization
  • Conclusions
  • Documents
  • Predefined

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