Shukla, Abhinav and Gullapuram, Shruti Shriya and Katti, Harish and Yadati, Karthik and Kankanhalli, Mohan and Subramanian, Ramanathan (2017) Affect Recognition in Ads with Application to Computational Advertising. In: 25th ACM International Conference on Multimedia, MM 2017, 23 October 2017 through 27 October 2017, Mountain View, pp. 1148-1156.
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Abstract
dvertisements (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.
| Item Type: | Conference Paper |
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| Publisher: | Association for Computing Machinery, Inc |
| Additional Information: | The Copyright of this article belongs to the Authors. |
| Keywords: | Advertisements; Affect recognition; Computational advertising; Convolutional neural networks (CNNs); Human and computational perception; Multimodal; Convolution; Neural networks; Advertisements; Affect recognition; Computational advertisings; Computational perception; Convolutional neural network; Multi-modal; Marketing |
| Department/Centre: | Division of Biological Sciences > Centre for Neuroscience |
| Date Deposited: | 24 Jun 2022 06:25 |
| Last Modified: | 24 Jun 2022 06:25 |
| URI: | https://eprints.iisc.ac.in/id/eprint/73480 |
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