Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 264-269.doi: 10.11896/jsjkx.200800116

• Intelligent Computing • Previous Articles     Next Articles

Aspect Sentiment Analysis of Chinese Online Course Review Based on Efficient Transformer

PAN Fang1, ZHANG Hui-bing2, DONG Jun-chao2, SHOU Zhao-yu3   

  1. 1 Teachers College for Vocational and Technical Education,Guangxi Normal University,Guilin,Guangxi 541004,China
    2 Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    3 School of Information and Communication,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:PAN Fang,born in 1982,master,asso-ciate professor.Her main research interests include educational big data and online learning behavior analysis.
    ZHANG Hui-bing,born in 1976,Ph.D,is a member of China Computer Federation.His main research interests include educational big data and social computing.
  • Supported by:
    National Natural Science Foundation of China(61662013,61967005,U1811264).

PDF (PC)

1134

Abstract: It is of great value for the healthy development of online courses that accurately mine the emotional information contained in online course reviews.Most of the existing research on sentiment analysis of Chinese online course reviews is a coarse-grained model,which cannot accurately express the fine-grained sentiment for all aspects of the review sentence.The paper puts forward an efficient Transformer based sentiment analysis model for Chinese online course review.Firstly,the dynamic word vector coding of the review's aspect and context is obtained by the Albert pre-training model.Then,the semantic representation of the review's aspect and context is carried out by the efficient Transformer which can input the word vector in parallel.Finally,it uses the interactive attention mechanism to learn the important parts of the context and aspects in the course review,and puts its final representation into the sentiment classification layer to predict the sentiment polarity.Experimental results on real datasets of MOOC in China show that the accuracy of the proposed model achieves more than 80% at lower time cost compared with the baseline model.

Key words: Aspect-based sentiment analysis, Attention mechanism, Course review, Online course, Pre-training language model

CLC Number: 

  • TP391
[1] WANG L,HU G,ZHOU T.Semantic analysis of learners' emotional tendencies on online MOOC education [J].Sustainability,2018,10(6):1921.
[2] MITEBAIDAL K,DELGADOVERA C,SOLÍSAVILÉS E,et al.Sentiment analysis in education domain:A systematic literature review [C]//International Conference on Technologies and Innovation.Springer,2018:285-297.
[3] MORENOMARCOS P M,ALARIOHOYOS C,MUÑOZ-MERINO P J,et al.Sentiment analysis in MOOCs:A case study [C]//Global Engineering Education Conference (EDUCON).IEEE,2018:1489-1496.
[4] SHARMA N,JAIN V.Evaluation and Summarization ofStudent Feedback Using Sentiment Analysis [C]//International Conference on Advanced Machine Learning Technologies and Applications.Springer.2020:385-396.
[5] HARRIS S C,KUMAR V.Identifying student difficulty in adigital learning environment [C]// International Conference on Advanced Learning Technologies (ICALT).IEEE,2018:199-201.
[6] BUENAÑOFERNÁNDEZ D,VILLEGASCH W,LUJÁN-MORA S.Using text mining to evaluate student interaction in virtual learning environments [C]// World Engineering Education Conference (EDUNINE).IEEE,2018:1-6.
[7] ORAMAS B S R,ZATARAIN C R,BARRÓN EM L,et al.Opinion mining and emotion recognition in an intelligent learning environment [J].Computer Applications in Engineering Education,2019,27(1):90-101.
[8] BARRONESTRADA M L,ZATARAINCABADA R,ORA-MASBUSTILLOS R.Emotion Recognition for Education using Sentiment Analysis [J].Research in Computing Science,2019,148(5):71-80.
[9] NGUYEN P X V,HONG T T T,VAN NGUYEN K,et al.Deep learning versus traditional classifiers on vietnamese students' feedback corpus [C]// NAFOSTED Conference on Information and Computer Science (NICS).IEEE,2018:75-80.
[10] LALATA J P,GERARDO B,MEDINA R.A Sentiment Analysis Model for Faculty Comment Evaluation Using Ensemble Machine Learning Algorithms [C]//International Conference on Big Data Engineering.ACM,2019:68-73.
[11] ONAN A.Sentiment analysis on massive open online course evaluations:A text mining and deep learning approach [J].Computer Applications in Engineering Education,2020,1002(5):22253.
[12] LAN Z,CHEN M,GOODMAN S,et al.Albert:A lite bert for self-supervised learning of language representations [J].arXiv:1909.11942.
[13] SOE N,SOE P T.Domain Oriented Aspect Detection forStudent Feedback System [C]// International Conference on Advanced Information Technologies (ICAIT).IEEE,2019:90-95.
[14] SHUOQIU Y,CHAOJUN X.Research on Constructing Sentiment Dictionary of Online Course Reviews based on Multi-source Combination [C]// International Conference on Data Science and Information Technology.ACM,2019:71-76.
[15] YUAN X.Emotional tendency of online legal course reviewtexts based on SVM algorithm and network data acquisition [J].Journal of Intelligent & Fuzzy Systems,2019,37(5):6253-6263.
[16] KANDHRO I A,WASI S,KUMAR K,et al.Sentiment Analysis of Student's Comment by using Long-Short Term Model [J].Indian Journal of Science and Technology,2019,12(8):1-16.
[17] DO H H,PRASAD P W C,MAAG A,et al.Deep learning for aspect-based sentiment analysis:a comparative review [J].Expert Systems with Applications,2019,118(3):272-299.
[18] ZHOU J,HUANG J X,CHEN Q,et al.Deep learning for aspect-level sentiment classification:Survey,vision,and challenges [J].IEEE Access,2019,7(5):78454-78483.
[19] DONG L,WEI F,TAN C,et al.Adaptive recursive neural network for target-dependent twitter sentiment classification [C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics,2014:49-54.
[20] TANG D,QIN B,FENG X,et al.Effective LSTMs for target-dependent sentiment classification [J].arXiv:1512.01100.
[21] LIU Q,ZHANG H,ZENG Y,et al.Content attention model for aspect based sentiment analysis [C]//Proceedings of the World Wide Web Conference.ACM,2018:1023-1032.
[22] WANG Y,HUANG M,ZHU X,et al.Attention-based LSTM for aspect-level sentiment classification [C]// Conference on Empirical Methods in Natural Language Processing.International World Wide Web Conferences Steering Committee,2016:606-615.
[23] MA D,LI S,ZHANG X,et al.Interactive attention networks for aspect-level sentiment classification [J].arXiv:1709.00893.
[24] DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding [J].arXiv:1810.104805.
[25] VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need [C]//Advances in Neural Information Processing Systems.Curran Associates Inc.2017:5998-6008.
[26] TAY Y,BAHRI D,METZLER D,et al.Synthesizer:Rethinking Self-Attention in Transformer Models [J].arXiv:2005.00743.
[27] KITAEV N,KAISER Ł,LEVSKAYA A.Reformer:The efficient transformer [J].arXiv:2001.04451v2.
[28] XU Q,ZHU L,DAI T,et al.Aspect-based sentiment classification with multi-attention network [J].Neurocomputing,2020,388(5):135-143.
[1] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[2] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[3] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[4] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[5] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[6] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[7] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[9] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[10] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[11] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[12] XIONG Luo-geng, ZHENG Shang, ZOU Hai-tao, YU Hua-long, GAO Shang. Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism [J]. Computer Science, 2022, 49(7): 212-219.
[13] PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun. Satellite Onboard Observation Task Planning Based on Attention Neural Network [J]. Computer Science, 2022, 49(7): 242-247.
[14] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[15] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.