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ELMo-ACSA based Multi-aspect View Mining of Students’ Reviews on Teaching

student reviews on teaching encompasses multiple teaching assessment aspects and different emotional tendencies. In this paper, we propose a multi-aspects view mining method for student reviews on teaching, combined with the teaching assessment index system of Xi’an Jiaotong University. The proposed method aims to mine the detailed and rich emotional information of student reviews on teaching, find the effectiveness of teachers and classes, identify the problems encountered classroom accurately, and provide technical support for intelligent teaching feedback. The multi-aspect view mining is divided into two parts: teaching assessment aspect definition and sentiment classification aspect, which are respectively based on the multi-level evaluation index of Xi’an Jiaotong University and the combination of pre-training language model and gated convolutional networks. Experimental verification was carried out on the longer assessment reviews collected from the big data platform for real-time monitoring of teaching quality. The F value of the proposed method is superior to the comparison model, indicating the effectiveness of the multi-aspect view mining method.