EmoFusionNet

Abstract

Social media platforms are vital spaces for emotional interaction in the age of digital communication, greatly impacting public opinion, behavioral patterns, and group responses to events. It presents EmoFusionNet, a complete and state-of-the-art computational framework created especially to simulate, examine, and forecast the ever-changing emotional flow in social media ecosystems. The system seamlessly combines a number of cutting-edge methods from generative modeling, deep learning, and graph theory to represent the complex interactions between communities, users, and changing emotions over time. Temporal graph neural networks are utilized to identify and monitor temporal user communities, groups of users whose emotional activities impact one another, in order to simulate the dynamic character of social media networks. The key users and characteristics that aid in the dispersion of emotions are the primary focus of EmoFusionNet's attention-enhanced hypergraph neural networks’ multi-head scaled dot-product attention mechanism. This guarantees that the model can dynamically learn how significant emotional trend-setters and contextual events produce pervasive mood shifts, even in complicated social situations. The novel technique models and forecasts the spread of emotions using a hybrid conditional generative adversarial network (cGAN) and hierarchical long short-term memory (LSTM) network. Thus, the cGAN component creates synthetic yet realistic emotional dispersion scenarios based on concepts from influencer profiles and their recent actions. Different scales of temporal interdependence can be addressed by the hierarchical LSTM module. It offers a window into mood-building or mood-rotting over days or weeks, in addition to predicting long-term sentiment patterns and reaction mining of current user sentiment (for instance, significant spikes in sentiment like happiness or rage after an event).