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Text mining in educational research = topic modeling and Latent Dirichlet Allocation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Text mining in educational research/ edited by Myint Swe Khine.
Reminder of title:	topic modeling and Latent Dirichlet Allocation /
other author:	Khine, Myint Swe.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	ix, 151 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Using the Structural Topic Model to Explore Learner Satisfaction with LMOOCs -- Text Mining Applications in Educational Research -- The Advent of Topic Noise Models -- Formalizing the Social Aspects of Topic Modeling: Focus on the Social Positioning of Researchers.
Contained By:	Springer Nature eBook
Subject:	Education - Research -
Online resource:	https://doi.org/10.1007/978-981-97-7858-4
ISBN:	9789819778584

Text mining in educational research = topic modeling and Latent Dirichlet Allocation /
Text mining in educational researchtopic modeling and Latent Dirichlet Allocation /[electronic resource] :edited by Myint Swe Khine. - Singapore :Springer Nature Singapore :2024. - ix, 151 p. :ill. (some col.), digital ;24 cm.

Using the Structural Topic Model to Explore Learner Satisfaction with LMOOCs -- Text Mining Applications in Educational Research -- The Advent of Topic Noise Models -- Formalizing the Social Aspects of Topic Modeling: Focus on the Social Positioning of Researchers.

This edited book consolidates and documents recent research on topic modeling in text mining using Latent Dirichlet Allocation (LDA). Written by leading experts in topic modeling, it covers a wide range of areas, such as theory building, systematic research, and innovative applications. This book offers a thorough exploration of the latest advancements in topic modeling. From identifying issues in unstructured text data to categorizing documents and extracting valuable insights, the book provides practical use of LDA as a powerful tool in qualitative and quantitative research. The chapters discuss the rapidly evolving landscape of topic modeling algorithms and offer tangible examples and applications of LDA in educational research, showcasing its real-world impact. This book dives into the heart of educational research and uncovers the transformative potential of Latent Dirichlet Allocation in shaping the future of topic modeling. This book is a valuable resource, highlighting exemplary works and rapid advances in the field. It appeals to students, researchers, and practitioners interested in text mining.

ISBN: 9789819778584

Standard No.: 10.1007/978-981-97-7858-4doiSubjects--Topical Terms:

523770
Education
--Research

LC Class. No.: LB1028

Dewey Class. No.: 370.721

Text mining in educational research = topic modeling and Latent Dirichlet Allocation /
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505 0 $a Using the Structural Topic Model to Explore Learner Satisfaction with LMOOCs -- Text Mining Applications in Educational Research -- The Advent of Topic Noise Models -- Formalizing the Social Aspects of Topic Modeling: Focus on the Social Positioning of Researchers.
520 $a This edited book consolidates and documents recent research on topic modeling in text mining using Latent Dirichlet Allocation (LDA). Written by leading experts in topic modeling, it covers a wide range of areas, such as theory building, systematic research, and innovative applications. This book offers a thorough exploration of the latest advancements in topic modeling. From identifying issues in unstructured text data to categorizing documents and extracting valuable insights, the book provides practical use of LDA as a powerful tool in qualitative and quantitative research. The chapters discuss the rapidly evolving landscape of topic modeling algorithms and offer tangible examples and applications of LDA in educational research, showcasing its real-world impact. This book dives into the heart of educational research and uncovers the transformative potential of Latent Dirichlet Allocation in shaping the future of topic modeling. This book is a valuable resource, highlighting exemplary works and rapid advances in the field. It appeals to students, researchers, and practitioners interested in text mining.
650 0 $a Education $x Research $x Methodology. $3 523770
650 0 $a Text data mining. $3 3495453
650 1 4 $a Digital Education and Educational Technology. $3 3538616
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