A Corpus for Sentiment Analysis and Emotion Recognition for a Learning Environment

In this paper we present the creation process of a corpus of phrases (opinions) related to learning computer programming. Opinions (textual phrases) are categorized in different emotions related to learning such as frustration, boring, excitement, and engagement. The results shows that in 851 opinions (754 Positives and 97 Negatives), there was a neutral, excited and engaged emotional tendency which indicates that we must include resources that induce students to write negative comments. We tested the corpus with a set of machine learning classifiers where the classifier with the highest score was Bernoulli Naïve Bayes with an accuracy of 76.77%.