Language that Captivates the Audience: Predicting Affective Ratings of TED Talks in a Multi-Label Classification Task

Elma Kerz, Yu Qiao, Daniel Wiechmann


Abstract
The aim of the paper is twofold: (1) to automatically predict the ratings assigned by viewers to 14 categories available for TED talks in a multi-label classification task and (2) to determine what types of features drive classification accuracy for each of the categories. The focus is on features of language usage from five groups pertaining to syntactic complexity, lexical richness, register-based n-gram measures, information-theoretic measures and LIWC-style measures. We show that a Recurrent Neural Network classifier trained exclusively on within-text distributions of such features can reach relatively high levels of overall accuracy (69%) across the 14 categories. We find that features from two groups are strong predictors of the affective ratings across all categories and that there are distinct patterns of language usage for each rating category.
Anthology ID:
2021.wassa-1.2
Volume:
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
April
Year:
2021
Address:
Online
Editors:
Orphee De Clercq, Alexandra Balahur, Joao Sedoc, Valentin Barriere, Shabnam Tafreshi, Sven Buechel, Veronique Hoste
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–24
Language:
URL:
https://aclanthology.org/2021.wassa-1.2
DOI:
Bibkey:
Cite (ACL):
Elma Kerz, Yu Qiao, and Daniel Wiechmann. 2021. Language that Captivates the Audience: Predicting Affective Ratings of TED Talks in a Multi-Label Classification Task. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 13–24, Online. Association for Computational Linguistics.
Cite (Informal):
Language that Captivates the Audience: Predicting Affective Ratings of TED Talks in a Multi-Label Classification Task (Kerz et al., WASSA 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wassa-1.2.pdf
Optional supplementary material:
 2021.wassa-1.2.OptionalSupplementaryMaterial.pdf