Research paper classification systems based on TF-IDF and LDA schemes

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order...

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Vydané v:Human-centric computing and information sciences Ročník 9; číslo 1; s. 1 - 21
Hlavní autori: Kim, Sang-Woon, Gil, Joon-Min
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 26.08.2019
Korea Information Processing Society, Computer Software Research Group
Predmet:
Algorithms
Artificial Intelligence
Big data
Classification
Cloud Computing for Human-centric Computing
Cluster analysis
Clustering
Communications Engineering
Computer Science
Computer Systems Organization and Communication Networks
Dirichlet problem
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IoT
Networks
Scientific papers
User Interfaces and Human Computer Interaction
Vector quantization
LDA
K-means clustering
TF-IDF
Paper classification
ISSN:2192-1962, 2192-1962
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Popis
Shrnutí:With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2192-1962
2192-1962
DOI:10.1186/s13673-019-0192-7