Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age‐, gender‐, race‐, or geography‐related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive performance in MOOC reviews, by pursuing the paradigms of ensemble learning and deep learning. In this contribution, we seek to answer several research questions on sentiment analysis on educational data. First, the predictive performance of conventional supervised learning methods, ensemble learning methods and deep learning methods has been evaluated. Besides, the efficiency of text representation schemes and word‐embedding schemes has been evaluated for sentiment analysis on MOOC evaluations. For the evaluation task, we have analyzed a corpus containing 66,000 MOOC reviews, with the use of machine learning, ensemble learning, and deep learning methods. The empirical analysis indicate that deep learning‐based architectures outperform ensemble learning methods and supervised learning methods for the task of sentiment analysis on educational data mining. For all the compared configurations, the highest predictive performance has been achieved by long short‐term memory networks in conjunction with GloVe word‐embedding scheme‐based representation, with a classification accuracy of 95.80%.

中文翻译：

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大规模开放在线课程评估的情感分析：一种文本挖掘和深度学习方法

大规模开放式在线课程（MOOC）是远程教育中的最新创新方法，可为参与者提供学习内容，而不受年龄，性别，种族或地理因素的影响。我们的研究目的是通过追求整体学习和深度学习的范例，在MOOC评论中提出一种具有高预测性能的有效情感分类方案。在此贡献中，我们试图回答有关教育数据情感分析的几个研究问题。首先，对常规监督学习方法，整体学习方法和深度学习方法的预测性能进行了评估。此外，已经对文本表示方案和词嵌入方案的效率进行了评估，以进行MOOC评估中的情感分析。对于评估任务，我们使用机器学习，整体学习和深度学习方法对包含66,000个MOOC评论的语料库进行了分析。实证分析表明，基于深度学习的体系结构在教育数据挖掘中的情感分析任务方面优于整体学习方法和监督学习方法。对于所有比较的配置，长期的短期存储网络与基于GloVe词嵌入方案的表示相结合，可实现最高的预测性能，分类精度为95.80％。实证分析表明，基于深度学习的体系结构在教育数据挖掘中的情感分析任务方面优于整体学习方法和监督学习方法。对于所有比较的配置，长期的短期存储网络与基于GloVe词嵌入方案的表示相结合，可实现最高的预测性能，分类精度为95.80％。实证分析表明，基于深度学习的体系结构在教育数据挖掘中的情感分析任务方面优于整体学习方法和监督学习方法。对于所有比较的配置，长期的短期存储网络与基于GloVe词嵌入方案的表示相结合，可实现最高的预测性能，分类精度为95.80％。