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

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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	PloS one Ročník 20; číslo 7; s. e0327347
Hlavný autor:	Zhang, Li
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 30.07.2025 Public Library of Science (PLoS)
Predmet:	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
ISSN:	1932-6203, 1932-6203
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	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 handling real-time demands hinder practical applications. This study proposes a hybrid news classification framework that integrates classical machine learning with modern advances in NLP to address these challenges. Our methodology introduces three key innovations: (1) Domain-Specific Feature Engineering, combining tailored n-grams and entity-aware TF-IDF weighting to amplify discriminative terms; (2) BERT-Guided Feature Selection, leveraging distilled BERT to identify contextually important words and resolve rare-term ambiguities; and (3) Computationally Efficient Deployment, achieving 95.2% of the accuracy of BERT at 1/52.4th of the inference cost. Evaluated on a balanced corpus of Sina News articles in 11 categories, the system demonstrates a test precision of 95.12% (vs. 84.43% for SVM+TF-IDF baseline), with statistically significant improvements confirmed by 5-fold cross-validation( p < 0.01). The critical findings reveal strong performance in distinguishing semantically distinct categories, while exposing challenges in fine-grained differentiation. The efficiency of the framework (2.1 inference latency) and scalability (linear utilization of CPU resources) validate its practicality for real-world deployment. This work bridges the gap between traditional feature engineering and transformer-based models, offering a cost-effective solution for news platforms. Future research will explore hierarchical classification and the adaptation of dynamic topics to further refine semantic boundaries.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0327347