educational big data, sentiment analysis, aspect-level, attention
As an important branch of natural language processing, sentiment analysis has received increasing attention. In teaching evaluation, sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to improve the quality of education and teaching. Aiming at the inefficiency and heavy workload of college curriculum evaluation methods, a Multi-Attention Fusion Modeling (Multi-AFM) is proposed, which integrates global attention and local attention through gating unit control to generate a reasonable contextual representation and achieve improved classification results. Experimental results show that the Multi-AFM model performs better than the existing methods in the application of education and other fields.
Tsinghua University Press
Guanlin Zhai, Yan Yang, Heng Wang et al. Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data. Big Data Mining and Anyalytics 2020, 3(4): 311-319.