NEWS AGGREGATOR WITH SENTIMENT ANALYSIS USING DEEP LEARNING

Newspapers, tabloids, and magazines gave way to digital forms of the news media like blogs, social media feeds, online news platforms, and other digital media formats. Fake news refers to a type of yellow press which intentionally presents misinformation or hoaxes spreading through bothtraditional print news media and recent online social media. In recent times, as a result of the booming as a result of the growth of online social networks, various political and commercial uses of fake news been popping up in large numbers and all over the internet. With deceptive words, online social network users can get infected by this online fake news easily, which has brought about already had significant effects on offline society. A significant objective for increasing trustworthiness of information in online social networks is to identify the fake news timely. The aim of this project is investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This undertaking addresses the difficulties brought on by the mysterious characteristics of fake news and diverse connections among news articles, creators and subjects. A novel automatic is introduced in this project. Fake news credibility inference model using deep learning algorithm. based on a collection of specific and deep diffusive model is constructed by deep learning algorithms using latent features derived from the textual information. network model to simultaneously learn the representations of news articles, authors, and subjects. Index Terms: Sentiment Analysis, Deep Learning, BERT, LSTM, CNN, Natural Language Processing (NLP), News Aggregator, Emotion Detection, Performance Metrics, Text Classification.