Exploring an Innovative Student Satisfaction Study on Social Media: A Method Combing Satisfaction Theory with Natural Language Processing Technology

Abstract

In the era of "Big data" and "Web 2.0", the viral speed of information dissemination magnified the data problem of subjectivity and timeliness in student satisfaction (SS) study. In this study, we conducted an innovative SS study based on a new data collection instrument which makes the data objective, timely, and even massive. Combining traditional Student Satisfaction Inventory (SSI) with frontier Natural Language Processing technology, we conducted the SS study based on social media. Instead of asking students to participate in, we used data mining and sentiment analysis technology to mine the existed data. Further, drawing from SSI, we proposed an SS analysis model using these collected data. To verify the validity of this method, we gave an empirical study.