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 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.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM)
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Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience. However, most computational affect recognition (AR) approaches examine ads via the text modality, and only limited work has been devoted to decoding ad emotions from audiovisual or user cues. This work (1) compiles an affective ad dataset capable of evoking coherent emotions across users; (2) explores the efficacy of content-centric convolutional neural network (CNN) features for AR vis-ã-vis handcrafted audio-visual descriptors; (3) examines user-centric ad AR from Electroencephalogram (EEG) responses acquired during ad-viewing, and (4) demonstrates how better affect predictions facilitate effective computational advertising as determined by a study involving 18 users. Experiments reveal that (a) CNN features outperform audiovisual descriptors for content-centric AR; (b) EEG features are able to encode ad-induced emotions better than content-based features; (c) Multi-task learning performs best among a slew of classification algorithms to achieve optimal AR, and (d) Pursuant to (b), EEG features also enable optimized ad insertion onto streamed video, as compared to contentbased or manual insertion techniques in terms of ad memorability and overall user experience.
Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to a ect recognition (AR) from ads. This work explicitly compares contentcentric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Speci cally, we (1) compile an a ective ad dataset capable of evoking coherent emotions across users; (2) explore the e cacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and nd that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the rst work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.
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