This paper is published in Volume-7, Issue-6, 2021
Area
Machine Learning, Computer Science
Author
Amisha Srivastava, Kushagra Jain
Org/Univ
Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh, India
Pub. Date
18 November, 2021
Paper ID
V7I6-1203
Publisher
Keywords
Text classification, Support Vector Machine (SVM), Neural Networks, Naive Bayes, Logistic Regression, Random Forest (RL), Decision Trees, Machine Learning Techniques, Sentiment Analysis

Citationsacebook

IEEE
Amisha Srivastava, Kushagra Jain. Role of machine learning in text classification – An extensive review, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Amisha Srivastava, Kushagra Jain (2021). Role of machine learning in text classification – An extensive review. International Journal of Advance Research, Ideas and Innovations in Technology, 7(6) www.IJARIIT.com.

MLA
Amisha Srivastava, Kushagra Jain. "Role of machine learning in text classification – An extensive review." International Journal of Advance Research, Ideas and Innovations in Technology 7.6 (2021). www.IJARIIT.com.

Abstract

Cyberspace has elevated business insights and created a virtual space to store all forms of information online. Due to the rapid development in the online world, the usage of digital documents has increased because it is comfortable for the users to share, update or keep track of the records in one place without losing data. However, maintaining massive data does not suit optimal decision-making and is extremely expensive for storage, processing, and collection. There is a gigantic possibility that human annotators make errors while classifying data because of distraction, monotony, fatigue, and failure to meet the requirements. Once the text classification method uses machine learning approaches, the process will execute with fewer mistakes and more accuracy. The main goal of this review paper is to highlight and explain the role of different machine learning methodologies in text classification. Concurrently, this paper describes the challenges faced by other machine learning techniques and text representation. Furthermore, this review paper will provide an extensive survey on how various machine learning techniques such as Neural Networks, Naive Bayes, Logistic Regression, Random Forest, Decision Trees, and Support Vector Machine (SVM) - are implemented in Text classification.
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