International Journal of Science, Engineering and TechnologyAuthors: Dr. Satender, Dr. Brij Mohan, Dr. Raj Kumar
Abstract: In Natural Language Processing (NLP), news classification is a key task that involves automatic categorization of news articles into predefined topics, which allows for efficient content organization and information retrieval. This paper presents a news classification model that is based on machine learning. Collecting news datasets, pre-processing text, extracting features using TF-IDF, and classification using multiple algorithms, including Naive Bayes, Logistic Regression, and Deep Learning (LSTM), are the main tasks of the proposed method. Technology, Business, Sports, Education, and Entertainment are among the categories included in the dataset. The model's evaluation is based on metrics such as accuracy, precision, recall, and F1-score. The LSTM model has experimental results that demonstrate its highest accuracy of 91%, surpassing Logistic Regression (87%) and Naive Bayes (85%). Automated content categorization in the media sector is furthered by the study.
DOI: http://doi.org/10.5281/zenodo.21455605
