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

Abdessamad Chanaa, Nour-eddine El Faddouli

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.

Keywords


Text sentiment classification; Hierarchical Attention Network (HAN); E-learning; Sentiment analysis; Online discussion forum; Massive Open Online Courses (MOOCs)

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Copyright (c) 2021 Abdessamad Chanaa, Nour-eddine El Faddouli


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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