Sentiment Analysis on Online Learning Comments via CNN-BiLSTM with Attention

Shuqin Huang ； Xiaoyan Shi ； Sulin Wei ； Yong Xu ； Hengna Wang

《World Scientific Research Journal》 8卷11期 (2022/11) Pp. 26-32

https://doi.org/10.6911/WSRJ.202211_8(11).0005

摘要

Sentiment analysis is an important part of natural language processing. Students' learning status can be evaluated by analyzing their sentiment in online course comments. The CNN‐BiLSTM with attention model was established to evaluate the students' emotional state. This model can avoid the problem that convolutional neural networks ignore the contextual meaning of word, traditional recurrent neural network is easy to cause gradient disappearance and LSTMs can’t prioritize individual sections of the sentence that are relevant. The experiments on real online course comments showed that our proposed model outperforms three baselines in accuracy.

關鍵字

CNN ； BiLSTM ； Attention mechanism ； Text sentiment analysis ； Online course comments

參考文獻

ZENUN KASTRATI,ALI SHARIQ IMRRAN,ARIANIT KURTI.Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs.IEEE ACCESS, vol.8(2020),p. 106799-106810

Alharbi ASM,de Dondker E.Twitter sentiment analysis with a deep neural network:An enhanced approach using user behavioral information.Cognitive Systems Research,(2019)No.54,p.50-61

C.S.Du,L.Huang.Sentiment Analysis Method based on Piecewise Convolutional Neural Network and Generative Adversarial Network.International Journal of Computers Communications & Control, vol.14(2019)No.1,p.7-20.

X. Li,Y.X. Ouyang,W. Rong,et al.A Shallow BERT-CNN Model for Sentiment Analysis on MOOCs Comments. 2019 IEEE International Conference on Engineering (Yogyakarta, Indonesia), Technology and Education,(2019)

P. Dedi,P. Khurana.Sentiment Analysis Using Fuzzy-Deep Learning. Proceedings of ICETIT 2019, Lecture Notes in Electrical Engineering,vol.605(2020),p.246-257.