Sentiment analysis in learning resources

Abstract

In this work, we aim to analyze the sentiments of learning resources from their textual contents. This work proposes a method for automatic assignment of emotional state to learning resources, based on their feature similarity with previously labeled learning resources. Then, various feature extraction strategies, which describe the relevant information in the texts, are compared for the task of sentiments analysis, considering the two main dimensions of emotions: arousal and valence. The results are very promising, showing a very high value in the performance metrics, like the R2 score.