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

Xiaomin Pu,Guangxi Yan,Chengqing Yu,Xiwei Mi,Chengming Yu

doi:10.3390/app112311313

Xiaomin Pu, Guangxi Yan + Show 3 more

Open Access

https://doi.org/10.3390/app112311313

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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.

Highlights

With the integration of internet technology into education and the expansion of the demand for online teaching in schools during the epidemic [1], online learning has been widely promoted to school education, professional education, party member education, and other educational learning classes
Multi-objective gray Wolf optimization is used to optimize the weight of the base model, and the ensemble of the two-channel word vector representation method with the convolutional neural network (CNN) and LSTM deep network is realized
As key data reflecting the quality of online courses, sentiment recognition of user reviews is of great significance for evaluation and online courses

Summary

Introduction

With the integration of internet technology into education and the expansion of the demand for online teaching in schools during the epidemic [1], online learning has been widely promoted to school education, professional education, party member education, and other educational learning classes. Diversified online courses, such as MOOC, SPOC, and NetEase Cloud Classes, have emerged. These online courses focus on providing personalized learning programs for different learners, and their convenient communication learning methods and advanced education concepts have triggered educational and teaching reforms at all levels of learning [2]. Some long-standing problems in online education, such as uneven quality of online courses and difficulty in guaranteeing learning effectiveness, need to be solved [3]. How to extract effective information for curriculum improvement and provide personalized learning suggestions for different users has attracted extensive attention from scholars and online education enterprises at home and abroad

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Results

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