Online educational platforms are enabling learners to consume a great variety of content and share opinions on their learning experience. The analysis of the sentiment behind such a collective intelligence represents a key element for supporting both instructors and learning institutions on shaping the offered educational experience. Combining Word Embedding representations and Deep Learning architectures has made possible to design Sentiment Analysis systems able to accurately measure the text polarity on several contexts. However, the application of such representations and architectures on educational data still appears limited. Therefore, considering the over-sensitiveness of the emerging models to the context where the training data is collected, conducting adaptation processes that target the e-learning context becomes crucial to unlock the full potential of a model. In this chapter, we describe a Deep Learning approach that, starting from Word Embedding representations, measures the sentiment polarity of textual reviews posted by learners after attending online courses. Then, we demonstrate how Word Embeddings trained on smaller e-learning-specific resources are more effective with respect to those trained on bigger general-purpose resources. Moreover, we show the benefits achieved by combining Word Embeddings representations with Deep Learning architectures instead of common Machine Learning models. We expect that this chapter will help stakeholders to get a clear view and shape the future research on this field.