The sentimental analysis relates to structural detection, extraction, quantification, and evaluation of effects and knowledge in natural language processing, text analysis, computer-language sociology, and biometric data. Many emotional hurdles prevent student-teacher communication's intellectual progress that can promote healthy feelings towards the class environment. Therefore, in this paper, sentimental analysis assisted student-teacher communication (SAA-STC) has been proposed for effective learning in higher education purposes. Using sentiment analysis, feelings were drawn from the students' teaching records based on the concurrent exploratory approach. STC is introduced, which can manage numerous e-learning fields to check the student-teacher communication for effective learning.STC allows any initial checks to be completed, focusing on a few transparent challenges to enable the framework to continue expanding higher education learning communication.

情感分析涉及自然语言处理、文本分析、计算机语言社会学和生物特征数据中的效果和知识的结构检测、提取、量化和评估。许多情感障碍阻碍了师生交流的智力进步，而这种交流可以促进对课堂环境的健康感受。因此，在本文中，情感分析辅助师生交流（SAA-STC）被提出用于高等教育中的有效学习。使用情感分析，基于并发探索法从学生的教学记录中提取情感。引入了STC，它可以管理众多的电子学习领域，以检查学生与教师之间的交流以实现有效学习。STC允许完成任何初步检查，