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Open Access

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

Guanlin Zhai, Yan Yang( ), Heng Wang, Shengdong Du
School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
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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 datasentiment analysisaspect-levelattention

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Big Data Mining and Analytics
Volume 3 Issue 4,
December 2020
Pages 311-319
DOI: 10.26599/BDMA.2020.9020024
Cite this article:
Zhai G, Yang Y, Wang H, et al. Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data. Big Data Mining and Analytics, 2020, 3(4): 311-319. https://doi.org/10.26599/BDMA.2020.9020024

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Received: 30 August 2020
Accepted: 30 September 2020
Published: 16 November 2020
© The authors 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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