Using Opinion Mining as an educational analytic: An integrated strategy for the analysis of students' feedback. (March 2021) - Misuraca, Michelangelo; Scepi, Germana; Spano, Maria
Highlights: Analysing students' textual feedback is substantial as well as analysing ratings. Students' mood can be very informative both for administrators and teaching staff. Opinion mining allows exploring text collections focusing on sentiment polarity. An automatic strategy is proposed to analyse the sentiment in students' comments. Abstract: The analysis of students' feedback written in natural language has been poorly considered in academic institutions, looking more frequently at students' ratings as a base to evaluate courses and instructors. Statistical text analyses offer the possibility of exploring text collections from a quantitative viewpoint. Particularly interesting is Opinion Mining (OM), a family of techniques at the crossroads of Statistics, Linguistics and Computer Science. OM allows evaluating the sentiment of individual opinions, highlighting their semantic orientation. In an educational context, this approach allows processing students' comments and creating powerful analytics. This paper aims at introducing readers to OM, presenting a strategy to compute the sentiment polarity of students' comments. After explaining the rationale of the proposal and its mathematical formalisation, a toy example is presented to show how it works in practice. A discussion about theoretical and empirical implications offers some hints about its potentiality in a learning environment.