Distance education learning systems have become one of the major investigation areas nowadays because the various categories of graduate learning are studied exclusively through the distance learning system. It provides the desired knowledge in various applications under the same domain or category in a well-organized manner. But the distance education learning system also has some major issues. In order to solve the problem of the distance education learning system, in this paper we present a novel sentiment analysis-based learning algorithm to learn the result of each learner in earlier classes and the level of each learner. The proposed sentiments analysis-based Fuzzy Neural Network learning methods analyze the results of previous classes’ positive and negative comments specified by the learner and the task result of the learner. Initially, to convey the message or information about the individual learner, the system is connected to the videoconferencing, and then the camera is connected to avoid delay problems during the conversation. To increase the teacher closeness and social occurrence, it proposes a learning method to review the comments of the previous classes and perform some of the tasks, such as taking tests on 10 min from previous classes and make a review on that the task based on the sentiment analysis mining methods to develop the learning participation, training efficiency, and value of communication in the distance education learning system. After the learning results are found from each one of the students in the class, they are sent to the teacher. The instructors and learners are exactly identified based on the face and speech recognition performed using the automation recognition system
