This paper presents a comparison among several sentiment analysis classifiers using three different techniques – machine learning, deep learning, and an evolutionary approach called EvoMSA – for the classification of educational opinions in an Intelligent Learning Environment called ILE-Java. To make this comparison, we develop two corpora of expressions into the programming languages domain, which reflect the emotional state of students regarding teachers, exams, homework, and academic projects, among others. A corpus called sentiTEXT has polarity (positive and negative) labels, while a corpus called eduSERE has positive and negative learning-centered emotions (engaged, excited, bored, and frustrated) labels. From the experiments carried out with the three techniques, we conclude that the evolutionary algorithm (EvoMSA) generated the best results with an accuracy of 93% for the corpus sentiTEXT, and 84% for the corpus eduSERE.

本文介绍了使用三种不同技术（机器学习，深度学习和一种称为EvoMSA的进化方法）在智能学习环境（称为ILE-Java）中对教育观点进行分类的几种情感分析分类器之间的比较。为了进行比较，我们在编程语言领域中开发了两种语料库，它们反映了学生对教师，考试，家庭作业和学术项目等的情感状态。名为sendiTEXT的语料库具有极性（正负）标签，而名为eduSERE的语料库具有以学习为中心的正面和负面情绪（订婚，激动，无聊和沮丧）标签。从这三种技术进行的实验中，