Revolutionizing News Genre Classification with a Novel Hybrid Sentiment Analysis Model

As online news content expands, the need for efficient news classification and analysis becomes increasingly critical. Sentiment analysis plays a key role in identifying article tone and audience reactions. Despite advancements in Natural Language Processing (NLP), existing models often struggle to capture subtle sentiment and contextual variations across genres. A hybrid model that combines BERT embeddings with Dense Neural Networks (DNN) addresses these challenges by leveraging BERT's deep contextual understanding and DNN's pattern recognition to improve classification accuracy. This approach overcomes the limitations of traditional and deep learning models, offering better performance in tasks like real-time filtering, personalized content recommendations, and misinformation detection, thus providing practical solutions for the evolving demands of news analysis.