Abstract Aiming at inefficiency and heavy workloads of college curriculum evaluation methods, a multi-aspect sentiment attention modeling(multi-ASAM) is proposed. Multi-ASAM concatenates a sentence and various aspects of the sentence by neural networks and adds emotional resources attention. To achieve better classification results, influence of relationships between aspects on emotinal polarity and contribution of emotional resources to emotional polarity is taken into auount in multi-ASAM. Experimental results show that Multi-ASAM is improved compared with other methods in the application of education and other fields.
Fund:Supported by National Natural Science Foundation of China(No.61572407), National Key Technology Research and Development Program(No.2015BAH19F02), Southwest Jiaotong University Key Education Reform Project(No.1802028)
Corresponding Authors:
YANG Yan, Ph.D., professor. Her research interests include artificial intelligence, big data analysis and mining, ensemble learning.
About author:: ZHAI Guanlin, master student. His research interests include data mining, deep learning and natural language processing;WANG Heng, master student. His research interests include deep learning and natural language processing;DU Shengdong, Ph.D., lecturer. His research interests include data mining, deep learning and big data analysis.
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2019, Vol. 32 
