Features extraction based on Naive Bayes algorithm and TF-IDF for news classification

• Published in: PloS one Vol. 20; no. 7; p. e0327347
• Main Author: Zhang, Li
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.07.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Bayes Theorem; Biology and Life Sciences; Classification; Classification systems; Computational linguistics; Computer and Information Sciences; Datasets; Deep learning; Documents; Electronic news gathering; Engineering and Technology; Humans; Identification and classification; Inference; Knowledge representation; Language processing; Latency; Machine Learning; Methods; Natural language interfaces; Natural Language Processing; Neural networks; News; Physical Sciences; Real time; Research and Analysis Methods; Semantics; Social Sciences; Statistical analysis; Support vector machines; Text categorization; China

The rapid proliferation of online news demands robust automated classification systems to enhance information organization and personalized recommendation. Although traditional methods like TF-IDF with Naive Bayes provide foundational solutions, their limitations in capturing semantic nuances and ha...