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Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews

  • Danilo DessíEmail author
  • Mauro Dragoni
  • Gianni Fenu
  • Mirko Marras
  • Diego Reforgiato Recupero
Chapter
  • 172 Downloads
Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

Online educational platforms are enabling learners to consume a great variety of content and share opinions on their learning experience. The analysis of the sentiment behind such a collective intelligence represents a key element for supporting both instructors and learning institutions on shaping the offered educational experience. Combining Word Embedding representations and deep learning architectures has made possible to design sentiment analysis systems able to accurately measure the text polarity on several contexts. However, the application of such representations and architectures on educational data still appears limited. Therefore, considering the over-sensitiveness of the emerging models to the context where the training data is collected, conducting adaptation processes that target the e-learning context becomes crucial to unlock the full potential of a model. In this chapter, we describe a deep learning approach that, starting from Word Embedding representations, measures the sentiment polarity of textual reviews posted by learners after attending online courses. Then, we demonstrate how Word Embeddings trained on smaller e-learning-specific resources are more effective with respect to those trained on bigger general-purpose resources. Moreover, we show the benefits achieved by combining Word Embeddings representations with deep learning architectures instead of common machine learning models. We expect that this chapter will help stakeholders to get a clear view and shape the future research on this field.

Keywords

Big data E-learning Deep learning Online education Sentiment analysis Word Embeddings Domain adaptation 

Notes

Acknowledgements

Danilo Dessì and Mirko Marras acknowledge Sardinia Regional Government for the financial support of their Ph.D. scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020, Axis III “Education and Training,” Specific Goal 10.5).

The research leading to these results has received funding from the EU’s Marie Curie training network PhilHumans—Personal Health Interfaces Leveraging HUman-MAchine Natural interactionS under grant agreement 812882.

Furthermore, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Danilo Dessí
    • 1
    Email author
  • Mauro Dragoni
    • 2
  • Gianni Fenu
    • 1
  • Mirko Marras
    • 1
  • Diego Reforgiato Recupero
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly
  2. 2.Fondazione Bruno KesslerTrentoItaly

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