Evaluating Neural Word Embeddings Created from Online Course Reviews for Sentiment Analysis


Social media are providing the humus for the sharing of knowledge and experiences and the growth of community activities (e.g., debating about different topics). The analysis of the user-generated content in this area usually relies on Sentiment Analysis. Word embeddings and Deep Learning have gathered extensive attention in various sentiment detection tasks. In parallel, the literature exposed the drawbacks of traditional approaches when content belonging to specic contexts is processed with general techniques. Thus, ad-hoc solutions are needed to improve the effectiveness of such systems. In this paper, we focus on user-generated content coming from the e-learning context to demonstrate how distributional semantic approaches trained on smaller context-specific textual resources are more eective with respect to approaches trained on bigger general-purpose ones. To this end, we build context-trained embeddings from online course reviews using state-of-the-art generators. Then, those embeddings are integrated in a deep neural network we designed to solve a polarity detection task on reviews in the e-learning context, modeled as a regression. By applying our approach on embeddings trained using background corpora from different contexts, we show that the performance is better when the background context is aligned with the regression context.