The Classification of News Articles Through the Use of Deep Learning and the Doc2Vec Modeling
- By Himanshu Makhija1
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View Affiliations Hide Affiliations1 Centre for Interdisciplinary Research in Business and Technology, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
- Source: Demystifying Emerging Trends in Machine Learning , pp 510-522
- Publication Date: February 2025
- Language: English
The Classification of News Articles Through the Use of Deep Learning and the Doc2Vec Modeling, Page 1 of 1
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The exponential growth in internet use has also led to the proliferation of textual information in large quantities. Since handling unstructured material manually is difficult, there is a need to explore novel techniques for automated categorization of textual information. The primary goal of text categorization is to teach a model to correctly categorise an unseen text. In this research, the Doc2vec word embedding technique was used to classify stories in Turkish from the TTC-3600 database of Turkish news and BBC news stories in English. In addition to the CNN based on deep learning, traditional machine learning classification methods including Gauss Naive Bayes (GNB), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) are used. The best classification results using CNN were achieved with the proposed model, scoring 94.17% on the Turkish database and 96.41% on the English database.
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