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Using natural language processing and machine learning algorithm for book categorization
- 1 University of California San Diego, La Jolla, California, 92093, USA
- 2 University of Science and Technology Beijing, Beijing, 100083, China
- 3 The Ohio State University, Columbus, Ohio 43210, USA
- 4 North Carolina State University, Raleigh, NC 27695, USA
- 5 North Carolina State University, Raleigh, NC 27695, USA
* Author to whom correspondence should be addressed.
Abstract
This research analyzed the relationship between multiple elements of a book's classifica-tion through natural language processing and machine learning. This paper used SVM and KNN to classify books according to the titles and author respectively. Also, books are categorized by summary through Decision Tree, Naïve Bayes and BERT. In the end, this work compared effects of these methods. Our findings show that 1) books have different levels of categorical characteristics in various parts of the book 2) through combining the title, author, and summary of the book, more accurate classification results were obtained 3) BERT achieved more accurate recognition compared to a variety of other algorithms used.
Keywords
natural language processing, machine learning, book classification
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Cite this article
Wang,D.;Tan,B.;Wei,M.;Cui,X.;Huang,X. (2023). Using natural language processing and machine learning algorithm for book categorization. Applied and Computational Engineering,2,78-89.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
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