Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks

Abstract

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.