山东理工大学学报（自然科学版）2024，Vol.38Issue(4)：45-52,8.
基于KMeans-EDA算法的非均衡评论情感分类研究
Research on non-balanced sentiment classification based on KMeans-EDA algorithm
摘要
Abstract
Real evaluation from learners is an important indicator to reflect the advantages and disadvan-tages of online courses,so it is very important to obtain learners'feedback quickly and accurately for the optimization of online courses.To dig deeper into the online earning behavior of learners,which in turn provide an effective data basis for online teaching,this study crawls the comment text from Chinese Uni-versity MOOC platform,set up a machine learning model based on self-attention pretrained Bert model,and perform accurate emotion classification of comment text so as to obtain the implicit emotional state of learners.Because there are few negative comments in the training data,an adaptive balanced resampling training strategy named KMeans-EDA was designed to solve the problem of model bias towards the major-ity class during training,which improves the model's ability to identify negative comments.The experi-ment shows that this strategy can increase the model F1-score of the comment text from 0.690 2 to 0.739 9.关键词
在线课程/评论文本/文本情感分类/预训练特征表示/非均衡训练Key words
online course/comment text/text sentiment classification/pretrained feature representation/unbalanced training分类
信息技术与安全科学引用本文复制引用
郭卡..基于KMeans-EDA算法的非均衡评论情感分类研究[J].山东理工大学学报（自然科学版）,2024,38(4):45-52,8.基金项目
安徽省高等学校自然科学研究项目(KJ2020A0818) （KJ2020A0818）
安徽外国语学院科研重点项目(AWky2020012) （AWky2020012）
