基于LSTM模型的学生反馈文本学业情绪识别方法
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摘要
分析学生学习过程产生的反馈文本,是发现其学业情绪的重要方式。传统的学业情绪测量方法主要包括使用学业情绪测量问卷和访谈分析,但这两种方法难以大规模地应用于在线教育环境。本研究旨在通过构建学业情绪自动预测模型,对大量学生反馈文本进行快速有效的学业情绪分类。研究首先利用词向量训练工具,将文本转化为多维向量;然后基于深度学习网络LSTM构建学业情绪预测模型,以文本的多维向量作为模型输入;最后经过多轮训练,优化模型参数。实验显示,上述模型可快速有效识别学生反馈文本中所包含的学业情绪,该模型在测试数据集上的学业情绪识别准确率可达89%。
Analyzing emotional texts produced by students in the learning process is an important way to discover students' academic emotions. The traditional method of measuring academic emotion is to use academic emotion questionnaire or interview approach, but this method is difficult to be widely used in an online learning environment. Therefore, this study aims to quickly and effectively discovering the implicit categories of academic emotions in a large number of student feedback texts by constructing an automatic predictive model of academic emotions. This paper first uses the word vector training tool to transform the text into a multi-dimensional vector. Then, based on the deep learning network LSTM, the academic sentiment prediction model is constructed. The model consists of two layers of LSTM, with the multidimensional vector of the text as input. Finally, after several rounds of training, optimizing Model parameters,Experiments show that the above model can quickly and effectively identify the academic sentiment contained in the student's feedback text. The accuracy of the school's academic sentiment in the test data set can reach 89%.
Analyzing emotional texts produced by students in the learning process is an important way to discover students' academic emotions. The traditional method of measuring academic emotion is to use academic emotion questionnaire or interview approach, but this method is difficult to be widely used in an online learning environment. Therefore, this study aims to quickly and effectively discovering the implicit categories of academic emotions in a large number of student feedback texts by constructing an automatic predictive model of academic emotions. This paper first uses the word vector training tool to transform the text into a multi-dimensional vector. Then, based on the deep learning network LSTM, the academic sentiment prediction model is constructed. The model consists of two layers of LSTM, with the multidimensional vector of the text as input. Finally, after several rounds of training, optimizing Model parameters,Experiments show that the above model can quickly and effectively identify the academic sentiment contained in the student's feedback text. The accuracy of the school's academic sentiment in the test data set can reach 89%.
引文
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[14] Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P.(2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ)[J]. Contemporary Educational Psychology, 36(1):36-48.
[15] Pekrun, R., Goetz, T., Titz, W., & Perry, R. P.(2002). Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research[J]. Educational Psychologist, 37(2):91-105.
[16] Quan, C., & Ren, F.(2009). Construction of a blog emotion corpus for Chinese emotional expression analysis[A]. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing[C]. Association for Computational Linguistics, Singapore: 1446-1454.
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[19] Singh, V. K., Piryani, R., Uddin, A., & Waila, P.(2013). Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification[A]. 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing[C]. IEEE, Kerala, India:712-717.
[20] Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank[A]. Proceedings of the 2013 conference on empirical methods in natural language processing[C]. Association for Computational Linguistics,Seattle,USA:1631-1642.
[21] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis[J]. Computational Linguistics, 37(2):267-307.
[22] 王瑞红(2009). 高职院校大学生学业情绪、自我与学业成就的相关研究[D]. 天津师范大学.
[23] 王瑞红(2010). 高职院校学生学业情绪与学业成就的相关研究[J]. 山西青年职业学院学报, 23(3): 98-100.
[24] 魏雪峰(2017). 问题解决与认知模拟——以数学问题为例[M].北京:中国社会科学出版社:99-100.
[25] 熊富林,邓怡豪,唐晓晟(2015). Word2vec的核心架构及其应用[J]. 南京师范大学学报(工程技术版),(1):43-48.
[26] 於雯,周武能(2018).基于LSTM的商品评论情感分析[J].计算机系统应用,27(8):159-163
[27] 俞国良,董妍(2005). 学业情绪研究及其对学生发展的意义[J]. 教育研究,(10):39-43.
[28] 赵淑媛,蔡太生(2012). 大学生学业情绪量表(AEQ)中文版的修订[J]. 中国临床心理学杂志, 20(4): 448-450.
[29] 郑啸,王义真,袁志祥,秦锋(2018).基于卷积记忆神经网络的微博短文本情感分析[J].电子测量与仪器学报, 32(03):195-200.
[30] 朱祖林,黄彩虹,李锐,洪婧倞(2011). 成人在线学业情绪倾向的测度方法研究[J]. 中国电化教育,(6):55-60.
[2] 董妍,俞国良(2007).青少年学业情绪问卷的编制及应用[J]. 心理学报, 39(5):852-860.
[3] Govaerts, S., & Grégoire, J. (2008). Development and construct validation of an academic emotions scale[J]. International Journal of Testing, 8(1):34-54.
[4] Graves,A. (2012). Long Short-Term Memory[A]. Supervised sequence labelling with recurrent neural networks[C]. Springer, Berlin, Heidelberg:37-45
[5] Hayat, A. A., Salehi, A., & Kojuri, J. (2018). Medical student’s academic performance: The role of academic emotions and motivation[J]. Journal of Advances in Medical Education & Professionalism, 6(4):168-175.
[6] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735-1780.
[7] 江波,李万健,李芷璇,叶韵(2018).基于面部表情的学习困惑自动识别法[J].开放教育研究, 24(04):101-108.
[8] John, F. K., & Stefan, C. K. (2001). Gradient flow in recurrent nets: The difficulty of learning long term dependencies[A]. A Field Guide to Dynamical Recurrent Networks[C] Wiley-IEEE Press:237-243.
[9] Li, C., Wu, H., & Jin, Q.(2014) Emotion classification of Chinese microblog text via fusion of bow and evector feature representations[A].Natural Language Processing and Chinese Computing[C].Springer, Berlin, Heidelberg: 217-228.
[10] 马惠霞(2008). 大学生一般学业情绪问卷的编制[J].中国临床心理学杂志, 16(6):594-596.
[11] 马惠霞,张泽民(2010). 大学生学业情绪成套问卷编制的理论构想[J]. 中国临床心理学杂志, 18(1):34-36.
[12] Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J.(2013). Distributed representations of words and phrases and their compositionality[A]. Advances in neural information processing systems [C]. Curran Associates Inc., Lake Tahoe, Nevada:3111-3119.
[13] Patrick, B. C., Skinner, E. A., & Connell, J. P.(1993). What motivates children's behavior and emotion? Joint effects of perceived control and autonomy in the academic domain[J]. Journal of Personality & Social Psychology, 65(4):781-791.
[14] Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P.(2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ)[J]. Contemporary Educational Psychology, 36(1):36-48.
[15] Pekrun, R., Goetz, T., Titz, W., & Perry, R. P.(2002). Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research[J]. Educational Psychologist, 37(2):91-105.
[16] Quan, C., & Ren, F.(2009). Construction of a blog emotion corpus for Chinese emotional expression analysis[A]. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing[C]. Association for Computational Linguistics, Singapore: 1446-1454.
[17] Rajan, A. P., & Victor, S.(2014). Web sentiment analysis for scoring positive or negative words using tweeter data[J].International Journal of Computer Applications, 96(6):33-37.
[18] Santos, C. D., & Gattit, M.(2014). Deep convolutional neural networks for sentiment analysis of short texts[A]. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics[C]. Dublin City University and Association for Computational Linguistics, Dublin, Ireland:69-78.
[19] Singh, V. K., Piryani, R., Uddin, A., & Waila, P.(2013). Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification[A]. 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing[C]. IEEE, Kerala, India:712-717.
[20] Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank[A]. Proceedings of the 2013 conference on empirical methods in natural language processing[C]. Association for Computational Linguistics,Seattle,USA:1631-1642.
[21] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis[J]. Computational Linguistics, 37(2):267-307.
[22] 王瑞红(2009). 高职院校大学生学业情绪、自我与学业成就的相关研究[D]. 天津师范大学.
[23] 王瑞红(2010). 高职院校学生学业情绪与学业成就的相关研究[J]. 山西青年职业学院学报, 23(3): 98-100.
[24] 魏雪峰(2017). 问题解决与认知模拟——以数学问题为例[M].北京:中国社会科学出版社:99-100.
[25] 熊富林,邓怡豪,唐晓晟(2015). Word2vec的核心架构及其应用[J]. 南京师范大学学报(工程技术版),(1):43-48.
[26] 於雯,周武能(2018).基于LSTM的商品评论情感分析[J].计算机系统应用,27(8):159-163
[27] 俞国良,董妍(2005). 学业情绪研究及其对学生发展的意义[J]. 教育研究,(10):39-43.
[28] 赵淑媛,蔡太生(2012). 大学生学业情绪量表(AEQ)中文版的修订[J]. 中国临床心理学杂志, 20(4): 448-450.
[29] 郑啸,王义真,袁志祥,秦锋(2018).基于卷积记忆神经网络的微博短文本情感分析[J].电子测量与仪器学报, 32(03):195-200.
[30] 朱祖林,黄彩虹,李锐,洪婧倞(2011). 成人在线学业情绪倾向的测度方法研究[J]. 中国电化教育,(6):55-60.