E-learning Text Sentiment Classification Using Hierarchical Attention Network (HAN)

Abdessamad Chanaa,Nour-Eddine El Faddouli

doi:10.3991/ijet.v16i13.22579

Abstract

Massive Open Online Courses (MOOCs) have recently become a very motivating research field in education. Analyzing MOOCs discussion forums presents important issues since it can create challenges for understanding and appropriately identifying student sentiment behaviours. Using the high effectiveness of deep learning, this study aims to classify forum posts based on their sentiment polarity using two experiments. The first use the three known sentiment labels (positive/negative/neutral) and the second one employs sevens labels. The classification method implemented the Hierarchical Attention Network (HAN) algorithm; it combines the attention mechanism with a hierarchical network that simulates the same hierarchical structure of the document. The analysis of 29604 discussion posts from Stanford University affirms the effectiveness of our model. HAN achieved a classification accuracy of 70.3%, which surpassed the other prediction results using usual text classification models. These results are promising and have implications on the future development of automated sentiment analysis tool on e-learning discussion forum.

Highlights

In recent years, the usage of discussion forums has increased exponentially; those platforms allow users to express thoughts and share opinions
In this paper, we study the sentiment factor expressed in those discussion forums using deep learning algorithms and text sentiment classification techniques, by creating a sentiment model dedicated to the educational field that could predict the sentiment orientation of each learner based on their posted messages in discussion forums
A comparison with other machine learning methods verifies the effectiveness of hierarchical attention network that reflect the hierarchical structure of the Massive Open Online Courses (MOOCs) reviews

Summary

Introduction

The usage of discussion forums has increased exponentially; those platforms allow users to express thoughts and share opinions. The use of e-learning discussion forums platforms has exponentially increased especially in the Massive Open Online Courses (MOOCs). MOOCs discussion forums enhanced the interactions between the learning process actors, where learners can textually communicate and interact, generating a tremendous amount of textual data related to each learner during his learning process. Text sentiment classification is one of the most decisive tasks in Natural Language Processing (NLP). It aims to automatically determine the sentiment orientation ( called sentiment polarity) of an analysed text, whether it is positive or negative, by iJET ‒ Vol 16, No 13, 2021

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