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Words that Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures

Words that Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures

Felix Weninger, Pascal Staudt, Björn Schuller
Copyright: © 2014 |Pages: 13
ISBN13: 9781466660427|ISBN10: 1466660422|EISBN13: 9781466660434
DOI: 10.4018/978-1-4666-6042-7.ch081
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MLA

Weninger, Felix, et al. "Words that Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures." Computational Linguistics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2014, pp. 1627-1639. https://doi.org/10.4018/978-1-4666-6042-7.ch081

APA

Weninger, F., Staudt, P., & Schuller, B. (2014). Words that Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures. In I. Management Association (Ed.), Computational Linguistics: Concepts, Methodologies, Tools, and Applications (pp. 1627-1639). IGI Global. https://doi.org/10.4018/978-1-4666-6042-7.ch081

Chicago

Weninger, Felix, Pascal Staudt, and Björn Schuller. "Words that Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures." In Computational Linguistics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1627-1639. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6042-7.ch081

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Abstract

In a large scale study on 843 transcripts of Technology, Entertainment and Design (TED) talks, the authors address the relation between word usage and categorical affective ratings of lectures by a large group of internet users. Users rated the lectures by assigning one or more predefined tags which relate to the affective state evoked in the audience (e. g., ‘fascinating', ‘funny', ‘courageous', ‘unconvincing' or ‘long-winded'). By automatic classification experiments, they demonstrate the usefulness of linguistic features for predicting these subjective ratings. Extensive test runs are conducted to assess the influence of the classifier and feature selection, and individual linguistic features are evaluated with respect to their discriminative power. In the result, classification whether the frequency of a given tag is higher than on average can be performed most robustly for tags associated with positive valence, reaching up to 80.7% accuracy on unseen test data.

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