Machine Learning Methods for Forecasting News Trends
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Abstract
News Classification project includes activities like data gathering and preprocessing, selection of appropriate algorithms like Random Forest, Logistic Regression, K Nearest Neighbor, Gaussian Naïve Bayes, Multinomial Naïve Bayes, Decision Tree Classifier training and evaluation of the model’s performance. We currently have a news-related dataset with a variety of data kinds, including political, sports, entertainment, and educational information. An expanding amount of information has to be categorized and arranged using automated techniques as the volume of electronic data grows dramatically. This research paper explores the application of machine learning approaches for news classification, aiming to categorize news articles into predefined topics or themes.The paper delves into the challenges posed by linguistic nuances, evolving language, and the need for adaptability in news classification systems. Evaluation metrics, including recall, F1- score, accuracy, and precision are investigated to evaluate the effectiveness of these systems. Real-world applications in information retrieval, recommendation systems, and personalized content delivery are highlighted, emphasizing the practical significance of news classification. The study concludes with a discussion on ongoing developments, emphasizing the impact of deep learning and pre-trained language models on the accuracy and efficiency of news classification systems.