Multisubject analysis and classification of books and book collections, based on a subject term vocabulary and the Latent Dirichlet Allocation

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Makris, N., Mitrou, N.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial Intelligence; Classification; Classification Algorithms; Digital Libraries; Dirichlet problem; Documents; E-books; Ensemble learning; Flattening; Formulas (mathematics); Latent Dirichlet Allocation; Mixtures; Multi-subject Classification; Physical sciences; Probabilistic logic; Probabilistic models; Probability distribution; Recurrent neural networks; Resource management; Statistical analysis; Statistical Natural Language Processing; Subject Headings; Task analysis; University Coursebooks; Vocabulary

In this paper, a new method for automatically analyzing and classifying books and book collections according to the subjects they cover is presented. It is based on a combination of the LDA method for discovering latent topics in the collection, on the one hand, and the description of subjects by me...