Abstract

In the context of the ever-growing influence of social media, understanding and predicting the popularity of content has become crucial for creators and marketers alike. Our research addresses this need by introducing a method to forecast the success of oral presentations, focusing on the nuanced use of paralinguistic features and insights derived from speech emotion recognition models. This innovative approach is designed to enhance verbal communication skills by providing public speakers with targeted feedback. We leverage a dataset of 2,462 TED talk videos, complete with metadata such as user comments, tags, and views, to establish a set of four objective metrics for determining presentation popularity. These metrics form the foundation of our analysis, enabling us to evaluate the efficacy of our predictive methodology. By integrating audio-based emotional cues with text-based content analysis we …

Date

September 15, 2024

Authors

Dimitrios Sgouropoulos, Petros Mitseas, Sofia Eleftheriou, Theodoros Giannakopoulos, Antonia Petrogianni, Lefteris Kapelonis, Nikolaos Antoniou, Athanasios Katsamanis, Shrikanth Narayanan

Conference

2024 12th International Conference on Affective Computing and Intelligent Interaction (ACII)

Pages

300-308

Publisher

IEEE