ISSN 2394-5125
 

Research Article 


APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING

K Ranjith Reddy, Dr. Sanjay Chaudhary.

Abstract
The feature selection method aims to select from the original feature representative subsets, by evaluating the
functionality of different features, focusing on reducing the dimension of the features while maintaining the
predictive precision of a classifier. Text documents may be divided into two broad categories of supervised and
unmonitored learning strategies by methods for classifying text documents. The controlled classification
procedure takes an object, usually as an input and outputs the desired value as a vector. A large number of
unknown objects can be examined in an unattended classification method.Text categorization involves a
learning methodology, whose applications include language identification, retrieval of information, opinion
extraction, spam filters and email routing, etc. Text categorisation may also be considered as a mechanism for
the labeling of different text documents of the natural corpus. Classification of text using various machine
teaching mechanisms meets the challenge of high vector attribute dimensionality. Therefore, it is very necessary
to remove noisy and irrelevant attributes from the function set vector by means of a feature selection technique
so the ML algorithms can work effectively.Feature selection has now played a major role in most efficient
system for spam detection, pattern recognition, automated organization, document management and information
retrieval. The most important task for an accurate classification is the selection of relevant features, and t he
overview of the text classification is used to achieve their objectives. Finally, the experimental results show that
the RDTFD approach based on independent features search is more robust and robust than other selection
methods.

Key words: The most important task for an accurate classification is the selection of relevant features, and t he overview of the text classification is used to achieve their objectives


 
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Pubmed Style

K Ranjith Reddy, Dr. Sanjay Chaudhary. APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING. JCR. 2020; 7(9): 3410-3417. doi:10.31838/jcr.07.09.524


Web Style

K Ranjith Reddy, Dr. Sanjay Chaudhary. APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING. http://www.jcreview.com/?mno=62179 [Access: May 30, 2021]. doi:10.31838/jcr.07.09.524


AMA (American Medical Association) Style

K Ranjith Reddy, Dr. Sanjay Chaudhary. APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING. JCR. 2020; 7(9): 3410-3417. doi:10.31838/jcr.07.09.524



Vancouver/ICMJE Style

K Ranjith Reddy, Dr. Sanjay Chaudhary. APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING. JCR. (2020), [cited May 30, 2021]; 7(9): 3410-3417. doi:10.31838/jcr.07.09.524



Harvard Style

K Ranjith Reddy, Dr. Sanjay Chaudhary (2020) APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING. JCR, 7 (9), 3410-3417. doi:10.31838/jcr.07.09.524



Turabian Style

K Ranjith Reddy, Dr. Sanjay Chaudhary. 2020. APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING. Journal of Critical Reviews, 7 (9), 3410-3417. doi:10.31838/jcr.07.09.524



Chicago Style

K Ranjith Reddy, Dr. Sanjay Chaudhary. "APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING." Journal of Critical Reviews 7 (2020), 3410-3417. doi:10.31838/jcr.07.09.524



MLA (The Modern Language Association) Style

K Ranjith Reddy, Dr. Sanjay Chaudhary. "APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING." Journal of Critical Reviews 7.9 (2020), 3410-3417. Print. doi:10.31838/jcr.07.09.524



APA (American Psychological Association) Style

K Ranjith Reddy, Dr. Sanjay Chaudhary (2020) APPLICATION AND ANALYSIS OF TEXT MINING IN MACHINE LEARNING. Journal of Critical Reviews, 7 (9), 3410-3417. doi:10.31838/jcr.07.09.524