Affect recognition in ads with application to computational advertising

Abhinav Shukla, Shruti Shriya Gullapuram, Harish Katti, Karthik Yadati, Mohan Kankanhalli, Ramanathan Subramanian

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

6 Citations (Scopus)

Abstract

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors [9] upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.

Original languageEnglish
Title of host publicationMM 2017 - Proceedings of the 2017 ACM Multimedia Conference
EditorsQiong Liu, Rainer Lienhart, Haohong Wang
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Pages1148-1156
Number of pages9
ISBN (Electronic)9781450349062
DOIs
Publication statusPublished - 23 Oct 2017
Externally publishedYes
Event25th ACM International Conference on Multimedia, MM 2017 - Mountain View, United States
Duration: 23 Oct 201727 Oct 2017

Publication series

NameMM 2017 - Proceedings of the 2017 ACM Multimedia Conference

Conference

Conference25th ACM International Conference on Multimedia, MM 2017
CountryUnited States
CityMountain View
Period23/10/1727/10/17

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