Subjective Evaluation of University Teaching Based on Text Analysis

Abstract

Abstract: Analyzing teaching subjective evaluation has important guiding significance for improving teaching quality. A five-latitude university teaching subjective evaluation sentiment analysis model is constructed to analyze the teaching subjective evaluation feedback text. And on the basis of this model, a K-nearest neighbor (KNN) based classification algorithm is proposed to realize the three classifications of“commendation, derogation, and neutrality”. The experimental results show that the subjective evaluation method of university teaching based on text analysis proposed in this paper can obtain a high classification accuracy.

Key words: Teaching evaluation, Text analysis, Sentiment analysis, Semantic rules, Machine learning, K-Nearest neighbor algorithm

CLC Number:

G640

DING Xuejun, GAN Tian, TIAN Yong. Subjective Evaluation of University Teaching Based on Text Analysis[J]. The Theory and Practice of Innovation and Enntrepreneurship, 2024, 7(9): 10-16.

References

[1] 刘贤友. 面向电子商务的评论文本情感分析研究[D]. 合肥:中国科学技术大学, 2015.
[2] BO P, LLLIAN L.Thembs up: sentiment cassification using machine leamning techniques[C]//Proceedings of the ACL-02 Conference on Empirical Methods in Naturral Language Processing-Volume 10. Association for Computational Linguistic, 2002: 79-86.
[3] REBECCA S H.Current use of student evaluations of teaching in anatomy education[J]. The Faseb Journal, 2020, 34(S1): 1.
[4] 罗美淑, 刘世勇, 夏春艳. 数据挖掘技术在教学评价中的应用研究[J]. 教育探索, 2013(2): 81-82.
[5] 汤小瑾. 从教学评价角度浅析当代美术课堂教学模式[J]. 西部皮革, 2019, 41(22): 160-161.
[6] 吴有昌, 高凌飚. SOLO分类法在教学评价中的应用[J]. 华南师范大学学报(社会科学版) , 2008(3): 95-99.
[7] 刘爽, 赵景秀, 杨红亚, 等. 文本情感分析综述[J]. 软件导刊, 2018, 17(6): 1-4,21.
[8] 陈平平, 耿笑冉, 邹敏, 等. 基于机器学习的文本情感倾向性分析[J]. 计算机与现代化, 2020(3): 77-81,92.
[9] 李丽华, 胡小龙. 基于深度学习的文本情感分析[J].湖北大学学报(自然科学版), 2020, 42(2): 142-149.
[10] 樊娜. 基于词项分布的特征选择和特征加权方法研究[D].长春:东北师范大学, 2017.
[11] 梅莉莉, 黄河燕, 周新宇, 等. 情感词典构建综述[J].中文信息学报, 2016, 30(5): 19-27.
[12] 杨立月, 王移芝.微博情感分析的情感词典构造及分析方法研究[J].计算机技术与发展, 2019, 29(2): 13-18.
[13] HATZIVASSILOGLOU V, MCKEOWN K R.Predicting the semantic orientation of adjectives[J]. Proceedings of the ACL, 1997: 174-181.
[14] ERCAN G,HAZIYEV F.Synset expansion on translation graph for automatic WordNet construction[J]. Information Processing and Management, 2019, 56(1): 130-150.
[15] 刘群, 李素建. 基于知网的词汇语义相似度的计算[C]. 第三届汉语词汇语义学研讨会(台北), 2002: 59-76.
[16] 徐琳宏, 林鸿飞, 潘宇, 等. 情感词汇本体的构造[J]. 情报学报, 2008, 27(2): 180-185.
[17] 曾小芹, 余宏. 基于Python的商品评论文本情感分析[J]. 电脑知识与技术, 2020, 16(8): 181-183.
[18] 王勇, 吕学强, 姬连春, 等. 基于极性词典的中文微博客情感分类[J]. 计算机应用与软件, 2014(1): 34-37.
[19] PERRUSQUÍA A, YU W. Robust control under worst‐case uncertainty for unknown nonlinear systems using modified reinforcement learning[J]. International Journal of Robust and Nonlinear Control, 2020, 30(7): 2920-2936.
[20] NIMA T,LAURA J, QIAN L, et al.Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation[J]. Medical Image Analysis, 2020, 63: 693-696.
[21] TOKTAM K, NOOSHIN R.Proposing feature engineering method based on deep learning and K-NNs for ECG beat classification and arrhythmia detection[J].Australasian Physical & Engineering Sciences in Medicine, 2020, 43(1): 49-68.