Sentiment Analysis and Topic Mining Using a Novel Deep Attention-Based Parallel Dual-Channel Model for Online Course Reviews-Reference-Cited by-同舟云学术

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Sentiment Analysis and Topic Mining Using a Novel Deep Attention-Based Parallel Dual-Channel Model for Online Course Reviews

Published:2022-12-05 Issue:1 Volume:15 Page:304-322
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Yan Chun,Liu Jiahui^ORCID,Liu Wei,Liu Xinhong

Funder

Natural Science Foundation of Shandong Province

National Bureau of Statistics of China

National Natural Science Foundation of China

General project of science and technology plan of Beijing Municipal Commission of Education

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-022-10083-7.pdf

Reference50 articles.

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2. Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N. Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Humaniz Comput. 2021;1–9. https://doi.org/10.1007/s12652-020-02623-6.

3. Cheng Y, Zhang X, Wang X, Zhao H, Yu Y, Wang X, de Pablos PO. Rethinking the development of technology-enhanced learning and the role of cognitive computing. Int J Semant Web Inf Syst (IJSWIS). 2021;17(1):67–96. https://doi.org/10.4018/IJSWIS.2021010104.

4. Tretyakov E, Savić D, Korpusenko A, Ionkina K. Sentiment analysis of social networks messages. In: Biologically Inspired Cognitive Architectures 2021. Springer International Publishing; 2022. pp. 552–560. https://doi.org/10.1007/978-3-030-96993-6_61.

5. Liao J, Tang J, Zhao X. Course drop-out prediction on MOOC platform via clustering and tensor completion. Tsinghua Sci Technol. 2019;24(4):412–422. https://doi.org/10.26599/TST.2018.9010110.

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