DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text-Reference-Cited by-同舟云学术

DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text

Published:2023 Issue: Volume:11 Page:32826-32841
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Bewong Michael¹^ORCID,Wondoh John²^ORCID,Kwashie Selasi³^ORCID,Liu Jixue²^ORCID,Liu Lin²^ORCID,Li Jiuyong²^ORCID,Islam Md. Zahidul¹^ORCID,Kernot David⁴

Affiliation:

1. School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW, Australia

2. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia

3. Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW, Australia

4. Defence Science Technology Group, Department of Defence, Edinburgh, SA, Australia

Funder

Charles Sturt University publication grant

Charles Sturt University Early Career Researcher Award 2021

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10083137.pdf?arnumber=10083137

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5. Improving LDA topic models for microblogs via tweet pooling and automatic labeling

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