Prediction of standing ovation of TED technology talks

Abstract

This research aims at the prediction of whether speeches of TED talk can cause audience standing ovation after the end of the talk. The phenomenon of audience standing ovation that we can see in TED talk is one of the objective evidence of the effect that speeches give to audience. We gathered TED talk data that we used as data to experiment the prediction. The methods of this present research consist of quantitative analysis according to speech content and machine learning technique by convolutional neural network. As a result, we achieved 77.11% accuracy and 0.63 F-measure from the prediction using TED talks of Technology topic. Our method used in this study is useful to predict occurrences of standing ovations, although improvement is necessary. Compared to other studies, our contribution, on the one hand, is that we focused on speech content as the effect of standing ovation. On the other hand, we incorporated quantitative analysis especially in terms of what features are effective to standing ovation and eventually apply those features to machine learning technique.