Learning from students' perception on professors through opinion mining

TL;DR

This paper employs sentiment analysis and machine learning to analyze students' open-ended opinions about classes, aiming to identify key topics and predict sentiments to improve teaching methodologies.

Contribution

It introduces a novel approach combining NLP and ML to analyze open-ended student feedback for identifying relevant topics and sentiment prediction.

Findings

Algorithms successfully predict sentiment with high accuracy

Identified key topics that influence student perceptions

Open-ended surveys provide rich insights into student opinions

Abstract

Students' perception of classes measured through their opinions on teaching surveys allows to identify deficiencies and problems, both in the environment and in the learning methodologies. The purpose of this paper is to study, through sentiment analysis using natural language processing (NLP) and machine learning (ML) techniques, those opinions in order to identify topics that are relevant for students, as well as predicting the associated sentiment via polarity analysis. As a result, it is implemented, trained and tested two algorithms to predict the associated sentiment as well as the relevant topics of such opinions. The combination of both approaches then becomes useful to identify specific properties of the students' opinions associated with each sentiment label (positive, negative or neutral opinions) and topic. Furthermore, we explore the possibility that students' perception surveys are carried out without closed questions, relying on the information that students can provide through open questions where they express their opinions about their classes.