Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.

中文翻译：

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教师在教育平台环境中使用社会信息的情绪分析

学习者的意见是重要的信息来源，可对教师和教育指导员有用，以改进学习程序和培训活动。通过分析学习者的行为并提取与其学习行为相关的数据，教育工作者可以指定适当的学习方法来激发学习者的兴趣，并有助于在课程期间建设性地监控学习进度或改进未来的课程。学习者生成的内容及其反馈和评论可以提供有关他们参加的教育程序和参加的培训活动的指示性信息。教育系统必须具备分析学习者评论的机制，并自动指定他们对课程和提供给他们的学习活动的意见和态度。本文描述了一个希腊语言情感分析系统，该系统分析用希腊语言编写的文本并生成特征向量，与分类算法一起，我们有机会根据个人意见和表达的满意度对希腊文本进行分类。情绪分析模块已集成到希腊学校网络的混合教育系统中，提供终身学习课程。该模块为参与培训程序并提供终身学习课程的讲师、政策制定者和教育机构提供了广泛的可能性，了解他们的学习者如何看待学习活动，并具体说明他们喜欢和不喜欢学习活动的哪些方面。实验研究显示了关于情感分析方法的性能以及用户意见和满意度的规范的非常有趣的结果。特征分析展示了有关为意见分析和嵌入结合深度学习方法提供指示性信息的特征的有趣发现，产生了令人满意的结果。