IDENTIFY STUDENT QUESTIONS ABOUT TRAINING INSTITUTIONS FROM ONLINE MEDIA POSTS

Automatically identifying and understanding students' questions about problems they encounter or issues related to their universities is very important for universities to promptly grasp the aspirations of their students. This enables them to support and satisfy their students and enhance their reputation. Especially as social networks and online media continue to develop, students can easily post their questions and concerns online. This makes it easier for universities to access and address student questions. Although this is not a new problem, it still faces many challenges due to issues in natural language processing. To address this problem, within the scope of this article, we conduct a survey, perform experiments, and propose a model to automatically classify students' questions into 11 areas of interest at the University of Transport and Communications. We conducted careful experiments with a dataset of more than ten thousand posts collected from websites, forums, and school fan pages. Finally, we obtained a model with prediction results that achieved an accuracy of over 85%.