Toward Better Education Quality through Students’ Sentiment Analysis Using AutoML

Corina Simionescu, Daniela Marcu, Marius Silviu Măciucă


Sentiment analysis from students' interactions with learning environments is a topic of interest for researchers in the field of education because it can make important contributions to improving the quality of instructional processes through recommendation systems integrated into learning applications, or by improving the quality of courses, by grouping students according to their common interests and providing feedback on school progress. There are two approaches to sentiment analysis: one lexicon-based and another that uses machine learning. In this study, we present a sentiment analysis from two own data sets that represent students' opinions about school. Our goal is to create a model that helps us to automatically label students' opinions, assigning sentiment scores between 0 and 4 (0 for an extremely negative opinion). To train and evaluate the performance of the model, we used opinions collected from 1443 Romanian high school students. The novelty that we propose is the manual labeling system. Our current research which uses a machine learning approach to classify students' opinions obtains an accuracy of 86.507%.



AutoML, Classification Models, Cloud Services, Education, Sentiment Analysis

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