A Recapitulation of Different Text Classification Algorithms
Riya Bajpai

Riya B, Department of Computer Science and Engineering, SRM Institute of Science and Technology, KTR Campus, Chennai, Tamilnadu, India.

Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3411-3414 | Volume-8 Issue-6, March 2020. | Retrieval Number: E6550018520/2020©BEIESP | DOI: 10.35940/ijrte.E6550.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Text classification process has gained a lot of importance in recent years and is still one of the most popular topics of discussion because of the presence of a huge outsized range of electronic documents from diverse resources. The text categorization process assigns predefined classes to documents. It finds noteworthy similarities in large textual data were interesting, hidden, previously unknown and extremely useful patterns and information can be discovered. Text classification helps in analysis of large textual data. Text mining intends facilitating customers extract informations from resources, and deals with operations such as retrieving, classifying ,cluster formation, mining of data, processing of natural language and techniques of machine learning together to classificate unalike patterns. Inside the process of content arrangement, terms gauging strategies configuration fitting loads to the offered terms to improvise content grouping execution. This paper overviews content order, the procedure of content grouping, distinctive term gauging techniques and correlations between various characterization calculations.
Keywords: Naïve Bayes, SVM, Text Mining, Text Classification, Random Forest Classifier
Scope of the Article: Web Algorithms.