Sentiment analysis of student online interaction in a blended postgraduate programme

Pham, Truman; Vo, Darcy; Lindsay, L.; Pashna, Mohsen; Li, F.; Baker, Karen; Han, Binglan; Rowley, Rich

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Date
2019-02
Citation:
Pham, T., Vo, D., Lindsay, L., Li, F., Pashna, M., Baker, K., Han, B., & Rowley, R. (2019, February). Sentiment Analysis of Student Online Interaction in a Blended Postgraduate Programme. Paper presented at the Scholarship of Technology Enhanced Learning (SoTEL) 2019, Auckland, New Zealand.
Permanent link to Research Bank record:
https://hdl.handle.net/10652/4604
Abstract
RESEARCH QUESTIONS: 1. To what extent do students interact online? 2. What are the sentiments in student online interaction? TML Blended Postgrad Programme Online interaction on G+ Community Sentiment analysis Google Natural Language Processing Research Methodology Data Collection and Analysis Overview of the collected data Results - Number of Posts and Comments Each Category Results - Monthly Posts and Comments Results - Monthly Average of Score and Magnitude of Posts and Comments Results - Box Plot for Sentiment Score in Each Category of G+ Community Results - Box Plot for Sentiment Magnitude in Each Category of G+ Community Discussion - What can we learn? Conclusion
Keywords:
blended learning, Mindlab by Unitec, student engagement, narrative analysis, sentiment analysis (SA), natural language processing
ANZSRC Field of Research:
130306 Educational Technology and Computing
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Authors
Available Online at:
https://sotel.nz/
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