Building Textual Fuzzy Interpretive Structural Modeling to Analyze Factors of Student Mobility Based on User Generated Content

Many factors influence student mobility across regions and countries. The roles of these factors, along with their interrelationship interaction, make a complex decision-making issue. textual data generated on social media can answer many open questions about affecting human behavior, particularly mobility. We have developed novel methodology, called Textual Fuzzy Interpretive Structural Modeling (TFISM), that automatically analyses large datasets to identify the internal external relationships between management or problems. This computational science methodology enhances (ISM) approaches allow input be data. It is multi-disciplinary integrates ISM Artificial Intelligence, Text extraction, information retrieval techniques. TFISM domain-free method, while we validated this two different from academic articles. In paper, present results our study critical most influential for global