EWNStream +: effective and real-time clustering of short text streams using evolutionary word relation network Yang, Shuiqiao, Huang, Guangyan, Zhou, Xiangmin, Mak, Vicky and Yearwood, John 2021, EWNStream +: effective and real-time clustering of short text streams using evolutionary word relation network, International journal of information technology and decision making, pp. 1-30, doi: 10.1142/S0219622021500024. Attached Files Name Description MIMEType Size Downloads Title EWNStream +: effective and real-time clustering of short text streams using evolutionary word relation network Author(s) Yang, Shuiqiao Huang, Guangyan orcid.org/0000-0002-1821-8644 Zhou, Xiangmin Mak, Vicky orcid.org/0000-0002-9306-5780 Yearwood, John orcid.org/0000-0002-7562-6767 Journal name International journal of information technology and decision making Start page 1 End page 30 Total pages 30 Publisher World Scientific Publishing Company Place of publication Singapore Publication date 2021-01-18 ISSN 0219-6220 1793-6845 Keyword(s) short text stream clustering topic discovery event detection Summary The real-time clustering of short text streams has various applications, such as event tracking, text summarization and sentimental analysis. However, accurately and efficiently clustering short text streams is challenging due to the sparsity problem (i.e., the limited information comprised in a single short text document leads to high-dimensional and sparse vectors when we represent short texts using traditional vector space models), topic drift and the fast generated text streams. In this paper, we provide an effective and real-time Evolutionary Word relation Network for short text streams clustering (EWNStream+) method. The EWNStream+ method constructs a bi-weighted word relation network using the aggregated term frequencies and term co-occurrence statistics at corpus level to overcome the sparsity problem and topic drift of short texts. Better still, as the query window in the stream shifts to the newly arriving data, EWNStream+ is capable of incrementally updating the word relation network by incorporating new word statistics and decaying the old ones to naturally capture the underlying topic drift in the data streams and reduce the size of the network. The experimental results on a real-world dataset show that EWNStream+ can achieve better clustering accuracy and time efficiency than several counterpart methods. Language eng DOI 10.1142/S0219622021500024 Indigenous content off Field of Research 0801 Artificial Intelligence and Image Processing 1503 Business and Management HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30147840 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 04 Feb 2021, 15:04:15 EST Fri, 05 Feb 2021, 04:01:39 EST Mon, 08 Feb 2021, 09:56:05 EST Tue, 09 Feb 2021, 05:04:09 EST Tue, 02 Mar 2021, 10:45:03 EST Tue, 02 Mar 2021, 10:47:03 EST Tue, 02 Mar 2021, 14:59:23 EST Sat, 17 Apr 2021, 02:30:12 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 39 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 08 Feb 2021, 09:56:05 EST

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