Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

Abstract

In recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course improvement. A new ensemble model is proposed for sentiment analysis. The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction. Moreover, the multi-objective gray wolf optimization (MOGWO) ensemble method is adopted to integrate the models mentioned above. The experimental results show that the sentiment recognition accuracy of the proposed model is higher than that of the other seven comparison models, with an F1score over 91%, and the recognition results of different emotion levels indicate the stability of the proposed ensemble model.

A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction

“…Non-parametric models mainly contain the support vector machine (SVM) and artificial neural networks, which can be regarded as artificial intelligence (AI) models [23], [24]. With the development of popular AI algorithms, the machine learning and deep learning models are widely used because of their powerful intelligent learning and fitting ability to complex data, which can use various optimization methods to update parameters and minimize the training error with fast speed [25], [26].…”