Sentiment analysis in student experiences of learning

Abstract

In this paper we present an evaluation of new techniques for automatically detecting sentiment polarity (Positive or Negative) in the students responses to Unit of Study Evaluations (USE). The study compares categorical model and dimensional model making use of five emotion categories: Anger, Fear, Joy, Sadness, and Surprise. Joy and Surprise are taken as a Positive polarity, whereas Anger, Fear and Sadness belong to Negative polarity in the binary classes, respectively. We evaluate the performances of category-based and dimension-based emotion prediction models on the 2,940 textual responses. In the former model, WordNet-Affect is used as a linguistic lexical resource and two dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA) and Non-negative Matrix Factorization (NMF). In the latter model, ANEW (Affective Norm for English Words), a normative database with affective terms, is employed. Despite using generic emotion categories and no syntactical analysis, NMF-based categorical model and dimensional model result in better performances above the baseline.