Design and category analysis of course review classification model based on voting and BiLSTM algorithm

Abstract

In order to extract the semantic information from a large number of student comments on the online education platform, this paper investigates and develops a Chinese comment classification model using the Voting and BiLSTM algorithm. The model classified the course reviews from two aspects of sentiment and content. The sentiment aspect was divided into three categories of “Positive,” “Negative,” and “Neutral,” and the content aspect was divided into three categories of “course,” “platform environment,” and “other.” In the collection and processing of data sets, an effective data set that accurately represents the characteristics of the education field is constructed by utilizing comments obtained from the NetEase Cloud course platform. The HuggingFace open source Bert pre-training model is then employed for word vector training. In the model construction, based on the Voting and BiLSTM model classification algorithm, a weighted fusion Bi_Voting strategy is proposed, and the classification principle based on SVM and the resampling enhance module are introduced. The experimental results show that the classification model has significant advantages in terms of accuracy, recall rate, and F1 value. In addition, to obtain more comprehensive information, we also conducted an in-depth analysis of different categories of reviews using hierarchical clustering and TextRank keyword extraction.