计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 264-269.doi: 10.11896/jsjkx.200800116
潘芳1, 张会兵2, 董俊超2, 首照宇3
PAN Fang1, ZHANG Hui-bing2, DONG Jun-chao2, SHOU Zhao-yu3
摘要: 准确挖掘在线课程评论中蕴涵的情感信息对在线课程的健康发展极具价值。现有中文在线课程评论情感分析研究大多为分析整条评论句子情感极性的粗粒度模型,无法准确表达课程评论句子中各个方面的细粒度情感。为此,提出一种基于高效Transformer的中文在线课程评论方面情感分析模型。首先,通过ALBERT预训练模型获得评论文本方面和上下文的动态字向量编码;然后,采用可以并行输入字向量的高效Transformer分别对课程评论文本的方面和上下文进行语义表征;最后,使用交互注意机制交互地学习课程评论文本中方面和上下文的重要部分,并输入方面和上下文的最终表示到情感分类层进行在线课程评论情感极性预测。在中国MOOC网真实数据集上的实验结果表明,高效Transformer中文在线课程评论方面情感分析模型与基线模型相比,在更低的时间开销下准确率达到了80%以上。
中图分类号:
[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] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[2] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
[3] | 戴禹, 许林峰. 基于文本行匹配的跨图文本阅读方法 Cross-image Text Reading Method Based on Text Line Matching 计算机科学, 2022, 49(9): 139-145. https://doi.org/10.11896/jsjkx.220600032 |
[4] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
[5] | 熊丽琴, 曹雷, 赖俊, 陈希亮. 基于值分解的多智能体深度强化学习综述 Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization 计算机科学, 2022, 49(9): 172-182. https://doi.org/10.11896/jsjkx.210800112 |
[6] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[7] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
[8] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[9] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
[10] | 汪鸣, 彭舰, 黄飞虎. 基于多时间尺度时空图网络的交通流量预测模型 Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction 计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188 |
[11] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
[12] | 熊罗庚, 郑尚, 邹海涛, 于化龙, 高尚. 融合双向门控循环单元和注意力机制的软件自承认技术债识别方法 Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism 计算机科学, 2022, 49(7): 212-219. https://doi.org/10.11896/jsjkx.210500075 |
[13] | 彭双, 伍江江, 陈浩, 杜春, 李军. 基于注意力神经网络的对地观测卫星星上自主任务规划方法 Satellite Onboard Observation Task Planning Based on Attention Neural Network 计算机科学, 2022, 49(7): 242-247. https://doi.org/10.11896/jsjkx.210500093 |
[14] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
[15] | 曾志贤, 曹建军, 翁年凤, 蒋国权, 徐滨. 基于注意力机制的细粒度语义关联视频-文本跨模态实体分辨 Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism 计算机科学, 2022, 49(7): 106-112. https://doi.org/10.11896/jsjkx.210500224 |
|