Sentiment Analysis from Students’ Feedback : A Romanian High School Case Study

Abstract

The education system is a source that generates significant amounts of data, daily, in various formats and, often, hiding valuable information. Finding a good way to unravel those hidden gems, represents one of the most challenging problems of natural language processing, namely sentiment analysis. This involves, applying NLP and text analysis techniques to identify and classify subjective opinions in different materials such as documents or sentences. In our work, we used as raw data, the opinions of students from eleven high schools in Suceava, related to various aspects of the educational process. They were collected through a Google Docs form, and analyzed through the Orange environment (an open source tool for machine learning and data visualization). In this paper, we make a comparative study of the obtained results using the Ekman and Plutchik models. Each model extracts from the analyzed texts, a different emotion, based on which the students’ sentiments towards the educational process will be analyzed.