Assessing the Effectiveness of Topic Modeling Algorithms in Discovering Generic Label with Description

dc.contributor.author	Rahman, Shadikur
dc.contributor.author	Hossain, Syeda Sumbul
dc.contributor.author	Arman, Md. Shohel
dc.contributor.author	Rawshan, Lamisha
dc.contributor.author	Toma, Tapushe Rabaya
dc.contributor.author	Rafiq, Fatama Binta
dc.contributor.author	Md. Badruzzaman, Khalid Been
dc.date.accessioned	2022-01-12T05:26:38Z
dc.date.available	2022-01-12T05:26:38Z
dc.date.issued	2020-02-13
dc.identifier.uri	http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6722
dc.description.abstract	Analyzing short text or documents using topic modeling becomes a popular solutions for the increasing number of documents produced in everyday life. For handling the large amount of documents, many topic modeling algorithms are used e.g. LDA, LSI, pLSI, NMF. In this study, we have used LDA, LSI, NMF and also lexical database wordNet synset for candidate labels in our topics labeling. And finally compare the effectiveness of topic modeling algorithms for short documents. Among those LDA gives the better result in terms of WUP similarity. This study will help to select the proper algorithm for labeling topics and can easily identify the meaning of topics.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Springer	en_US
dc.subject	Topic modeling	en_US
dc.subject	LDA	en_US
dc.subject	NMF	en_US
dc.subject	LSI	en_US
dc.subject	Topic labeling	en_US
dc.title	Assessing the Effectiveness of Topic Modeling Algorithms in Discovering Generic Label with Description	en_US
dc.type	Article	en_US