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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM17188

Tag Suggestion System Using Multi-Label Text Categorization

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM17188

Volume:

6 |

12 |

Dec 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RJ

R. JOSHITHA

DDK

Dr. DURGESH KUMAR

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Abstract

The study of text analysis is a relatively new discipline. The technique of evaluating and extracting information from textual data is already being used in a variety of fields, including marketing, product management, academics, and governance. The act of selecting acceptable categories from a predefined set and applying them to natural language texts is known as text classification or text categorization. Text classification, to put it simply, is the process of obtaining general tags from unstructured textual content. The generation of all generic tags is done using a list of pre-defined groupings. Users will find it easier to find and navigate your website or application if you categorize your information and items. We’ll be working on a text classification model in this article that examines the textual description of a query and generates numerous labels for it. Multi-Label Text Classification, a subset of multiple output models, will be used to construct a multilabel text classification strategy for a tag recommendation system.

KEYWORDS

text classification categorization natural language processing textual data generic tags.

Cite This Article

R. JOSHITHA, Dr. DURGESH KUMAR, "Tag Suggestion System Using Multi-Label Text Categorization," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 12, Dec 2022.

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Publication Date

12/18/2022

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The authors declare no conflict of interest related to this research work.

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