Fake News Detection Using Machine Learning and Sentiment Analysis Integrated in a Flask Web Application

Fake or misleading information, spreading quickly on media, is influencing the public perception and is stirring up social and political turmoil. The traditional manual verification methods could be more scalable given the sheer content volume. Machine learning techniques have been used in the past for research in this area for the detection of fake news, but their usefulness has often been limited by low prediction accuracy and reliance on specific feature sets. The present research work proposes a comprehensive methodology that combines machine learning classification algorithms along with feature extraction techniques to detect fake news. Utilizing a random forest classifier and TF-IDF vectorization, the system is implemented as a user-friendly web application using Flask. To gain more insight into the emotional aspects of the news, the sentiment analysis has been integrated in the present work. The results show the efficacy/usefulness of the implemented system, which achieves high accuracy in predicting the fake news with a clear presentation of the confidence levels and emotional tone. The findings suggest that this approach is better than the previous methods because it significantly enhances the precision of fake news detection and offers practical applications for real-time use on digital platforms.