Chat analysis to understand students using text mining

Abstract

Network communication has been the main method in people communication. The goal is to help educators grasping the students' social and psychological status and personal characteristics by mining conversation contents of a student group. In this paper, the text mining is used to find hot topics in chat groups, personal language behavior, personal motivation, and members of the emotions involved in the overall performance trends and the personal emotional expression trend. By using word frequency analysis, co-occurrence word frequency analysis, and lexical emotional similarity analysis, experimental results show that the method can quickly and effectively grasp the characteristics of subjects for students to develop strategies provide the basis for education. © 2012 Springer-Verlag.