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Automatic MOOC video classification using transcript features and convolutional neural networks

Chatbri, Houssem, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, Zhou, Jiang orcid logoORCID: 0000-0002-3067-8512, Kameyama, Keisuke, Kwan, Paul and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2017) Automatic MOOC video classification using transcript features and convolutional neural networks. In: ACM Multimedia 2017 - MultiEdTech Workshop, 23-27 Oct 2017, Mountain View, CA, USA. ISBN 978-1-4503-5508-7

Abstract
The amount of MOOC video materials has grown exponentially in recent years. Therefore, their storage and analysis need to be made as fully automated as possible in order to maintain their management quality. In this work, we present a method for automatic topic classification of MOOC videos using speech transcripts and convolutional neural networks (CNN). Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical co-occurrence transformation that we designed. Finally, a CNN is used to produce video category labels for a transcript image input. For our data, we use the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is strongly competitive against other methods that are also based on transcript features and supervised learning.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:MOOC video classification; transcript features; convolutional neural networks (CNN)
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the 2017 ACM Workshop on Multimedia-based Educational and Knowledge Technologies for Personalized and Social Online Training. . ACM. ISBN 978-1-4503-5508-7
Publisher:ACM
Official URL:https://doi.org/10.1145/3132390.3132393
Copyright Information:© 2017 ACM This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2017 ACM Workshop on Multimedia-based Educational and Knowledge Technologies for Personalized and Social Online Training
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21907
Deposited On:27 Oct 2017 10:01 by Houssem Chatbri . Last Modified 02 Oct 2019 15:21
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