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Autori principali: Keskin, Kerem, Keleş, Mümine Kaya
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2507.21058
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author Keskin, Kerem
Keleş, Mümine Kaya
author_facet Keskin, Kerem
Keleş, Mümine Kaya
contents In this study, book summaries and categories taken from book sites were classified using word embedding methods, natural language processing techniques and machine learning algorithms. In addition, one hot encoding, Word2Vec and Term Frequency - Inverse Document Frequency (TF-IDF) methods, which are frequently used word embedding methods were used in this study and their success was compared. Additionally, the combination table of the pre-processing methods used is shown and added to the table. Looking at the results, it was observed that Support Vector Machine, Naive Bayes and Logistic Regression Models and TF-IDF and One-Hot Encoder word embedding techniques gave more successful results for Turkish texts.
format Preprint
id arxiv_https___arxiv_org_abs_2507_21058
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Categorical Classification of Book Summaries Using Word Embedding Techniques
Keskin, Kerem
Keleş, Mümine Kaya
Computation and Language
Artificial Intelligence
In this study, book summaries and categories taken from book sites were classified using word embedding methods, natural language processing techniques and machine learning algorithms. In addition, one hot encoding, Word2Vec and Term Frequency - Inverse Document Frequency (TF-IDF) methods, which are frequently used word embedding methods were used in this study and their success was compared. Additionally, the combination table of the pre-processing methods used is shown and added to the table. Looking at the results, it was observed that Support Vector Machine, Naive Bayes and Logistic Regression Models and TF-IDF and One-Hot Encoder word embedding techniques gave more successful results for Turkish texts.
title Categorical Classification of Book Summaries Using Word Embedding Techniques
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2507.21058