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

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dc.contributor.authorRahman, Shadikur
dc.contributor.authorHossain, Syeda Sumbul
dc.contributor.authorArman, Md. Shohel
dc.contributor.authorRawshan, Lamisha
dc.contributor.authorToma, Tapushe Rabaya
dc.contributor.authorRafiq, Fatama Binta
dc.contributor.authorMd. Badruzzaman, Khalid Been
dc.date.accessioned2022-01-12T05:26:38Z
dc.date.available2022-01-12T05:26:38Z
dc.date.issued2020-02-13
dc.description.abstractAnalyzing 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.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6722
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6722
dc.language.isoen_US
dc.publisherSpringer
dc.sourceDIU Institutional Repository
dc.subjectTopic modeling
dc.subjectLDA
dc.subjectNMF
dc.subjectLSI
dc.subjectTopic labeling
dc.titleAssessing the Effectiveness of Topic Modeling Algorithms in Discovering Generic Label with Description
dc.typeArticle

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