Automatic MOOC Video Classification using Transcript Features and Convolutional Neural Networks
Houssem Chatbri, Kevin M. McGuinness, Suzanne Little, Jiang Zhou, Keisuke Kameyama, Paul Kwan, Noel Edward O'Connor · 2017
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.