A Two-Level Learning Hierarchy of Nonnegative …

A Two-Level Learning Hierarchy of NonnegativeMatrix factorization based Topic Modeling forMain Topic ExtractionHendri MurfiDepartment of Mathematics, Universitas IndonesiaDepok 16424, modeling is a type of statistical model that has beenproven successful for tasks including discovering topics and their trendsover time. In many applications, documents may be accompanied bymetadata that is manually created by their authors to describe the se-mantic content of documents, titles and tags. A proper way of in-corporating this metadata to topic modeling should improve its perfor-mance. In this paper, we adapt a Two-Level Learning Hierarchy method forincorporating the metadata into Nonnegative matrix factorization basedtopic modeling. Our experiments on extracting main topics show that themethod improves the interpretability scores and also produces more in-terpretable topics than the baseline one-level Learning Hierarchy :topic modeling, Nonnegative matrix factorization , incorpo-rating metadata, Nonnegative least squares, main topic extraction1 IntroductionAs our collection of digital documents continues to be stored and gets huge, wesimply do not have the human power to read all of the documents to providethematic information.

A Two-Level Learning Hierarchy of Nonnegative Matrix Factorization Based Topic Modeling for Main Topic Extraction Hendri Mur Department of …

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  Based, Topics, Matrix, Hierarchy, Hierarchy of nonnegative matrix factorization based topic, Nonnegative, Factorization

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