Title :
Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback
Author :
Altrabsheh, Nabeela ; Cocea, Mihaela ; Fallahkhair, Sanaz
Author_Institution :
Sch. of Comput., Univ. of Portsmouth, Portsmouth, UK
Abstract :
Students´ real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy.
Keywords :
computer aided instruction; learning (artificial intelligence); support vector machines; teaching; SVM; automatic analysis; education; educational domain; machine learning techniques; neutral class; real-time students feedback; sentiment analysis; support vector machines; teaching; unigrams; Accuracy; Analytical models; Education; Niobium; Real-time systems; Sentiment analysis; Support vector machines; Educational Data Mining; Feature Selection; Real-time Feedback; Sentiment Analysis;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on
Conference_Location :
Limassol
DOI :
10.1109/ICTAI.2014.70