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《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (9): 108-118.doi: 10.6040/j.issn.1671-9352.0.2023.334

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基于在线评论的线上教学平台选择方法

梁霞,郭洁*   

  1. 山东财经大学管理科学与工程学院, 山东 济南 250014
  • 发布日期:2024-10-10
  • 通讯作者: 郭洁(1998— ),女,博士研究生,研究方向为管理与决策分析等. E-mail:guojie@mail.sdufe.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(72201155);山东省社科规划项目(21CGLJ12);山东省高等学校青年创新团队发展计划项目(2021RW020)

A method of online teaching platform selection based on online reviews

LIANG Xia, GUO Jie*   

  1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, Shandong, China
  • Published:2024-10-10

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摘要： 为了更好地选择线上教学平台,给予大学生更好的线上课程学习体验,并为今后的线上教育教学提供参考,提出一种基于在线评论的线上教学平台选择方法。首先,利用爬虫技术搜集部分线上教学平台的用户评论,采用NLPIR-ICTCLAS汉语分词系统进行分词。再运用TF-IDF算法提取属性词,并结合人工挑选的方法获得属性集合,利用均方差法确定属性的权重。然后,对在线评论进行情感分析,将用户情感倾向表示为关于评价标度的概率分布。在此基础上,通过扩展的VIKOR法进行方案排序,选出最优线上教学平台。最后,通过实例和对比分析证明了本文所提方法的可行性。

关键词: 线上教学平台, 在线评论, 情感分析, TF-IDF算法

Abstract: To better select online teaching platforms, give college students a better online course learning experience, and provide a reference for future online education and teaching, a method for selecting online teaching platforms based on online reviews is proposed. Firstly, user reviews from alternative online teaching platforms are collected by the crawler technology, and NLPIR-ICTCLAS Chinese word separation system is used to separate online words. Next, attribute word extraction is conducted using TF-IDF algorithm, along with a method that was manually selected to obtain the attribute set. The weights of attributes are determined using the mean square deviation method. Subsequently, sentiment analysis is carried out on the online reviews, with user emotional orientations represented as probability distributions regarding the evaluation scale. On this basis, the extended VIKOR method is used to select the optimal online teaching platform. Finally, the feasibility of the method proposed in this paper is demonstrated through an example and comparative analysis.

Key words: online teaching platform, online review, sentiment analysis, TF-IDF algorithm

中图分类号: 

  • C934
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