eprintid: 8534
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/85/34
datestamp: 2023-11-09 16:20:26
lastmod: 2023-11-09 16:20:26
status_changed: 2023-11-09 16:12:53
type: conference_item
metadata_visibility: show
creators_name: Zubir, W.M.A.M.
creators_name: Aziz, I.A.
creators_name: Jaafar, J.
title: A survey on textual semantic classification algorithms
ispublished: pub
keywords: Classification (of information); Statistics, Classification algorithm; Latent Dirichlet allocation; Latent Semantic Analysis; Research papers; Scientific community; Semantic classification, Semantics
note: cited By 1; Conference of 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 ; Conference Date: 16 November 2017 Through 17 November 2017; Conference Code:134594
abstract: This paper provides a broad overview of three popular textual semantic classification algorithms used both in the industry and in the scientific community. The three algorithms are TF-IDF, Latent Semantic Analysis and Latent Dirichlet Allocation. We selected these three algorithms because they are the foundation of semantic classification and they are still widely used in the field of semantic classification. Firstly, this paper exhibits the inner workings of each of the algorithm both in the original authors intuition and the mathematical model utilized. Next, we discuss the advantages of each of the algorithms based on recent and credible research papers and articles. We also critically dissect the limitations of each of the algorithms. Lastly, we provide a general argument on the way forward in improving of the algorithms. This paper aims to give a general understanding on these algorithms which we hope will spur more research in improving the field of semantic classification. © 2017 IEEE.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047420059&doi=10.1109%2fICBDAA.2017.8284098&partnerID=40&md5=8693fde163723518787fff06ac204563
id_number: 10.1109/ICBDAA.2017.8284098
full_text_status: none
publication: 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017
volume: 2018-J
pagerange: 1-6
refereed: TRUE
isbn: 9781538607909
citation:   Zubir, W.M.A.M. and Aziz, I.A. and Jaafar, J.  (2017) A survey on textual semantic classification algorithms.  In: UNSPECIFIED.