Clustering scientific documents with topic modeling

C-K Yau; A. Porter; N. Newman; Arho Suominen

doi:10.1007/s11192-014-1321-8

Clustering scientific documents with topic modeling

C-K Yau
, A. Porter
, N. Newman
, Arho Suominen^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

220 Citations (Scopus)

Abstract

Topic modeling is a type of statistical model for discovering the latent "topics" that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities

Original language	English
Pages (from-to)	767-786
Journal	Scientometrics
Volume	100
Issue number	3
DOIs	https://doi.org/10.1007/s11192-014-1321-8
Publication status	Published - 2014
MoE publication type	A1 Journal article-refereed

Keywords

Topic modeling
text analysis
latent dirichlet allocation

Access to Document

10.1007/s11192-014-1321-8

Cite this

@article{1ea5651f249f4aef966499238412fd13,

title = "Clustering scientific documents with topic modeling",

abstract = "Topic modeling is a type of statistical model for discovering the latent {"}topics{"} that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities",

keywords = "Topic modeling, text analysis, latent dirichlet allocation",

author = "C-K Yau and A. Porter and N. Newman and Arho Suominen",

note = "Project code: CEK ",

year = "2014",

doi = "10.1007/s11192-014-1321-8",

language = "English",

volume = "100",

pages = "767--786",

journal = "Scientometrics",

issn = "0138-9130",

publisher = "Akademiai Kiado",

number = "3",

}

TY - JOUR

T1 - Clustering scientific documents with topic modeling

AU - Yau, C-K

AU - Porter, A.

AU - Newman, N.

AU - Suominen, Arho

N1 - Project code: CEK

PY - 2014

Y1 - 2014

N2 - Topic modeling is a type of statistical model for discovering the latent "topics" that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities

AB - Topic modeling is a type of statistical model for discovering the latent "topics" that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities

KW - Topic modeling

KW - text analysis

KW - latent dirichlet allocation

U2 - 10.1007/s11192-014-1321-8

DO - 10.1007/s11192-014-1321-8

M3 - Article

SN - 0138-9130

VL - 100

SP - 767

EP - 786

JO - Scientometrics

JF - Scientometrics

IS - 3

ER -

Clustering scientific documents with topic modeling

Abstract

Keywords

Access to Document

Fingerprint

Cite this