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Questgator: A Platform for Content Aggregation and Text Classification


Article Information

Title: Questgator: A Platform for Content Aggregation and Text Classification

Authors: Abdul Latif Shaikh, Fizza Abbas Alvi, Babar Ali, Ubaidullah Rajput, Hadi Bux

Journal: VAWKUM Transactions on Computer Sciences

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30

Publisher: VFAST-Research Platform

Country: Pakistan

Year: 2022

Volume: 10

Issue: 2

Language: English

DOI: 10.21015/vtcs.v10i2.1342

Categories

Abstract

The Web has witnessed a surge in content over recent years. Content is revolutionizing the way people conduct business, communicate, and make informed decisions. However, the vast amount of data used for communication today is oftenunstructured and challenging to comprehend. Content aggregators provide a solution to this problem by collecting data from various sources and organizing it into a structured format in one place. This research proposed the content aggregator "Questgator" that extracts content for example news, scholarships, jobs, books, video content, and research papers. In this paper Naive Bayes theorem is used for text classification. Moreover, paper also provides comparison with other platforms to show the efficiency of proposed content aggregator.


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