Enhanced detection of fabricated news through sentiment analysis and text feature extraction

Amidst the rapid dissemination of news, preventing misleading information is crucial. Using sentiment score, count vectorization, Term frequency and inverse document frequency, this study presents an innovative way for recognizing misleading information. Across three datasets eg. Covid-19, LIAR and Politifact, the proposed method evaluated accuracy and F1-score using different machine learning techniques. For 2-class classification on the LIAR dataset, the model achieves a 20% performance boost compared to state-of-the-art deep learning and machine techniques, and shows a significant 30% improvement for 6-class classification. In addition, compared to deep learning methods, machine learning approaches provide better accuracy and F1-score while requiring less time to complete. This study offers a potential strategy to tackle the spread of false news in the modern information ecosystem.