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Predicting TED Talk Ratings from Language and Prosody
May 21, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Md Iftekhar Tanveer, Md Kamrul Hassan, Daniel Gildea, M. Ehsan Hoque
arXiv ID
1906.03940
Category
cs.MM: Multimedia
Cross-listed
cs.CL
Citations
2
Venue
arXiv.org
Last Checked
3 months ago
Abstract
We use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers. Our dataset contains over 2200 TED Talk transcripts (includes over 200 thousand sentences), audio features and the associated meta information including about 5.5 Million ratings from spontaneous visitors of the website. We propose three neural network architectures and compare with statistical machine learning. Our experiments reveal that it is possible to predict all the 14 different ratings with an average AUC of 0.83 using the transcripts and prosody features only. The dataset and the complete source code is available for further analysis.
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