Social influence algorithms and emotion classification for Prediction of Human Behavior: A Survey

Emergence of big data is directly proportional to the data shared in social media. Audio, video, text or the combination of all the above are the data shared in social media. Social networking is achieved by Social Networking Sites (SNS). In real world business, analysts use software tools to analyze product sales, promotion of brand and also tend to identify influential factors that impact their business. In this paper, the authors present the evolution and importance of social networks. Majority of the research work on influence models and algorithms are based on greedy algorithms and relay on influence models like Independent Cascade (IC) model, Linear Threshold (LT) model etc. The research survey presented here gives the overview of influence in social networks and human behavior that includes both cooperative and non-cooperative nature. The limitations in influencing users and the networks used are discussed in this survey and the objective is to explore current research issues in human behavior prediction from social networks.