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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.


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Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data

Show Author's information Guanlin ZhaiYan Yang( )Heng WangShengdong Du
School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China

Abstract

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.

Keywords:

educational big data, sentiment analysis, aspect-level, attention
Received: 30 August 2020 Accepted: 30 September 2020 Published: 16 November 2020 Issue date: December 2020
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Publication history

Received: 30 August 2020
Accepted: 30 September 2020
Published: 16 November 2020
Issue date: December 2020

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© The authors 2020

Acknowledgements

The research work was partially supported by the National Natural Science Foundation of China (No. 61976247) and Southwest Jiaotong University Education Reform Project (No. 20201010).

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