Hybrid Model for Human Behavior Recognition Using Emotions, Sentiments, and Mood Features

While social networking is a powerful communication tool, the obscured behavior of individuals on social networks remains a significant problem for users. Currently, research work is being focused on formulating mechanisms to determine the obscured behavior of users for secure and trustworthy social media. The proposed model employs mathematical formulation and multinomial classification of mood and emotions to analyze the conduct of an individual, thus enabling social trust on social media. First, natural language processing techniques are applied to predict the emotions, moods, and sentiments of an individual from the text, and then a mathematical model is applied to gather a comprehensive picture of one’s behavior using calculations at numerous instants. Finally, a subsequent trust state log is built in terms of positive and negative states which show the devotion in behavior in terms of mood, sentiments, and more significantly emotions. The efficiency of the proposed work has been demonstrated using simulation-based and real-world datasets along with individual behavior graphs for various conversations