A Definitive Survey of How to Use Unsupervised Text Classifiers

Sachin Sharma, J. Adlin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A tremendous number of discussions, reviews, and postings are placed on online communities any day, greatly expanding the global dataset. To learn what consumers think of a particular service or brand, one must now sift thru vast volumes of data. The majority of assessments are written in English, however as electronics both men's comprehension advance, there is an increasing amount of Gujarati content available online. Additionally, understanding viewpoints about a single item is crucial to Indian language trend analysis because we display for all their viewpoints as entirely valid. We utilised the Punjabi collection for basic news items from various news outlets to increase the precision of our classifications. Dnn but other algorithms svm classifiers, such as Unusual Forest, Svms, and Rapid deployment Regressors, were used to explore the exactness of classifiers.

Original languageEnglish
Title of host publicationProceedings of the 2022 11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022
EditorsRakesh Kumar Dwivedi, Ashendra Kr. Saxena, Gulista Khan, Shambhu Bhardwaj
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages200-204
Number of pages5
ISBN (Electronic)9781665487320
DOIs
Publication statusPublished - 2022
Event11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022 - Moradabad, India
Duration: 16 Dec 202217 Dec 2022

Publication series

NameProceedings of the 2022 11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

Conference

Conference11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022
Country/TerritoryIndia
CityMoradabad
Period16/12/2217/12/22

Keywords

  • Classification
  • DNN
  • Logistic Regression Approaches
  • Machine Learning
  • Natural Language Processing
  • Random Forest
  • Support Vector Machines

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