A Review of Deep Learning Based Sentimental Approach to Identifying Counterfeit Files in Social Networking

False information spreading widely has the potential to be extremely harmful to people and communities. The proliferation of false news has coincided with the increased use of social media and the Internet as platforms for the exchange of ideas and information. The potential harm that false news could cause to people’s views and social interactions has made it an issue of great concern. Researchers and providers of social media services have increasingly turned to AI methods in recent years to combat the proliferation of disinformation. Most current methods for detecting false news rely on the assumption that the entire information propagation network structure is known before any learning algorithms are implemented. This paper summarizes recent research that has used deep learning to address issues in sentiment analysis, Finally, an in-depth analysis was run to contrast the experiment outcomes using various models and input attributes.