Decoding viewer emotions in video ads.

ABSTRACT: Understanding and predicting viewers' emotional responses to videos has emerged as a pivotal challenge due to its multifaceted applications in video indexing, summarization, personalized content recommendation, and effective advertisement design. A major roadblock in this domain has been the lack of expansive datasets with videos paired with viewer-reported emotional annotations. We address this challenge by employing a deep learning methodology trained on a dataset derived from the application of System1's proprietary methodologies on over 30,000 real video advertisements, each annotated by an average of 75 viewers. This equates to over 2.3 million emotional annotations across eight distinct categories: anger, contempt, disgust, fear, happiness, sadness, surprise, and neutral, coupled wit

SUBMITTER: Antonov A 

PROVIDER: S-EPMC11530461 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature