Mining Dynamics of Research Topics Based on the Combined LDA and WordNet

A large volume of research documents are available online for us to access and analysis. It is very important to detect and mine the dynamics of the research topics from these large corpora. In this paper, we propose an improved method by introducing WordNet to LDA. This approach is to find latent t...

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Main Authors: Chao Li, Sen Feng, Qingtian Zeng, Weijian Ni, Hua Zhao, Hua Duan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8580532/
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author Chao Li
Sen Feng
Qingtian Zeng
Weijian Ni
Hua Zhao
Hua Duan
author_facet Chao Li
Sen Feng
Qingtian Zeng
Weijian Ni
Hua Zhao
Hua Duan
author_sort Chao Li
collection DOAJ
description A large volume of research documents are available online for us to access and analysis. It is very important to detect and mine the dynamics of the research topics from these large corpora. In this paper, we propose an improved method by introducing WordNet to LDA. This approach is to find latent topics of large corpora, and then we propose many methods to analyze the dynamics of those topics. We apply the methodology to two large document collections: 1940 papers from NIPS 00-13 (1987–2000) and 2074 papers from NIPS 14-23 (2001–2010). Six experiments are conducted on the two corpora and the experimental results show that our method is better than LDA in finding research topics and is feasible in discovering the dynamics of research topics.
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spelling doaj.art-b20bf825cec94d6881a0c7c6974b81182022-12-21T18:35:59ZengIEEEIEEE Access2169-35362019-01-0176386639910.1109/ACCESS.2018.28873148580532Mining Dynamics of Research Topics Based on the Combined LDA and WordNetChao Li0https://orcid.org/0000-0002-3131-2723Sen Feng1Qingtian Zeng2Weijian Ni3Hua Zhao4Hua Duan5College of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, ChinaA large volume of research documents are available online for us to access and analysis. It is very important to detect and mine the dynamics of the research topics from these large corpora. In this paper, we propose an improved method by introducing WordNet to LDA. This approach is to find latent topics of large corpora, and then we propose many methods to analyze the dynamics of those topics. We apply the methodology to two large document collections: 1940 papers from NIPS 00-13 (1987–2000) and 2074 papers from NIPS 14-23 (2001–2010). Six experiments are conducted on the two corpora and the experimental results show that our method is better than LDA in finding research topics and is feasible in discovering the dynamics of research topics.https://ieeexplore.ieee.org/document/8580532/Research topics miningdynamics of research topicslatent Dirichlet allocationWordNetlarge corpora
spellingShingle Chao Li
Sen Feng
Qingtian Zeng
Weijian Ni
Hua Zhao
Hua Duan
Mining Dynamics of Research Topics Based on the Combined LDA and WordNet
IEEE Access
Research topics mining
dynamics of research topics
latent Dirichlet allocation
WordNet
large corpora
title Mining Dynamics of Research Topics Based on the Combined LDA and WordNet
title_full Mining Dynamics of Research Topics Based on the Combined LDA and WordNet
title_fullStr Mining Dynamics of Research Topics Based on the Combined LDA and WordNet
title_full_unstemmed Mining Dynamics of Research Topics Based on the Combined LDA and WordNet
title_short Mining Dynamics of Research Topics Based on the Combined LDA and WordNet
title_sort mining dynamics of research topics based on the combined lda and wordnet
topic Research topics mining
dynamics of research topics
latent Dirichlet allocation
WordNet
large corpora
url https://ieeexplore.ieee.org/document/8580532/
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