Abstract
Online course review can objectively reflect the emotional tendency of learners towards the learning effect. This paper proposes a deep neural network based sentiment analysis model for MOOC course reviews. The model uses Bidirectional Long Short-Term Memory Network (BiLSTM) to analyze Chinese semantic. In order to deal with the imbalance of training data set, this paper introduces two methods to balance it and adds dropout mechanism to prevent the over fitting of the model. The model is then applied to the emotional evaluation of MOOC course of “Fundamentals of College Computer Application”. The application results show that the model has achieved good accuracy and can well realize the emotional orientation analysis of online course reviews so as to provide valuable reference for Course Builders.
Highlights
With the rapid development of Internet technology, MOOC's online courses have attracted more and more attention from educators
Considering that the amount of data may not meet the needs of model parameter training, the text continues to crawl through the MOOC platform of Chinese universities to obtain 5037-course reviews of "computer foundation" courses offered by other universities
According to the needs of the current online course quality evaluation and Optimization Reform in Colleges and Universities, this paper proposes a sentiment analysis model of MOOC course evaluation based on Bidirectional Long Short-Term Memory Network (BiLSTM)
Summary
With the rapid development of Internet technology, MOOC's online courses have attracted more and more attention from educators. The application of deep text classification methods such as LSTM model to sentiment analysis of MOOC online review data is relatively less, and its application effect needs to be further verified. This paper will use the deep learning method to construct the MOOC course review's emotion analysis model. The online review data of the "Fundamentals of College Computer Application "MOOC course is combined to verify its effect in evaluating emotional orientation, . These application results of the two-text data balance processing methods are compared and analyzed to find the effective means of personalized auxiliary teaching and course evaluation of MOOC online course
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