Topic Modeling and Latent Dirichlet Allocation Khine, Myint Swe. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) text si 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access IX, 151 p. 38 illus., 26 illus. in color. online resource. 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. . 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. edited by Myint Swe Khine. Educational technology Teachers Training of Digital Education and Educational Technology Teaching and Teacher Education LB1028.3 9789819778584 https://doi.org/10.1007/978-981-97-7858-4 https://doi.org/10.1007/978-981-97-7858-4 250113 20251030114924.0 978-981-97-7858-4