Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

Cheng Wang; Sirui Huang; Ya Zhou

doi:10.1051/matecconf/202133605008

All issues

Volume 336 (2021)

MATEC Web Conf., 336 (2021) 05008

References

Open Access

Issue		MATEC Web Conf. Volume 336, 2021 2020 2^nd International Conference on Computer Science Communication and Network Security (CSCNS2020)


Article Number		05008
Number of page(s)		8
Section		Computer Science and System Design
DOI		https://doi.org/10.1051/matecconf/202133605008
Published online		15 February 2021

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[1] A. Oscar, Z. Gang Gao, and A. Carlos Iglesias., Knowledge-Based Systems,A semantic similarity-based perspective of affect lexicons for sentiment analysis. 165, 346-359 (2019) [Google Scholar]

[2] S. Kim, and H. Eduard main conference., Identifying and analyzing judgment opinions. Proceedings of the human language technology conference of the NAACL, (2006) [Google Scholar]

[3] X, G. xian, et al., IEEE Access, Chinese text sentiment analysis based on extended sentiment dictionary, 7, 43749-43762(2019) [Google Scholar]

[4] C. Hung., Information Processing & Management, Word of mouth quality classification based on contextual sentiment lexicons., 53, 751-763 (2017) [Google Scholar]

[5] B, K. Rushlene, 3rd International Conference on Computing for Sustainable Global Development, Opinion mining and sentiment analysis (IEEE, 2016) [Google Scholar]

[6] Y. Cai, K. Yang, et al., International Journal of Machine Learning and Cybernetics, A hybrid model for opinion mining based on domain sentiment dictionary, 1-12 (2019) [Google Scholar]

[7] F. Long, K. Zhou, W. Ou, IEEE Access, Sentiment Analysis of Text Based on Bidirectional LSTM with Multi-head Attention., 99, 1-1(2019) [Google Scholar]

[8] W. Jin, Y. Li-Chieh, et al., IEEE/ACM Transactions on Audio, Speech, and Language Processing, Tree-structured regional CNN-LSTM model for dimensional sentiment analysis,28, 581-591 (2019) [Google Scholar]

[9] L, Q. V, and T. Mikolov., International conference on machine learning Distributed representations of sentences and documents (2014) [Google Scholar]

[10] X. QianNan, L. Zhu, T. Dai., Neurocomputing, Aspect-based sentiment classification with multi-attention network, 388, 135-143(2020) [Google Scholar]

[11] D. Jacob, C. Ming-Wei, L. Kenton., Bert: Pre-training of deep bidirectional transformers for language understanding, 1810-04805 (2018) [Google Scholar]

[12] M. Marcos, P. Manuel., IEEE Global Engineering Education Conference (EDUCON). IEEE, Sentiment analysis in MOOCs: A case study (2018) [Google Scholar]

[13] T. Duyu, Q. Bing, F. Xiaocheng, Computer ence, Effective LSTMs for target-dependent sentiment classification, 1512-01100 (2015) [Google Scholar]

[14] M. Jiang, W. Zhang, M. Zhang, Journal of Computational Methods in Sciences and Engineering, An LSTM-CNN attention approach for aspect-level sentiment classification, 19, 859-868 (2019) [CrossRef] [Google Scholar]