  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-\~a-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
content-based or manual insertion techniques in terms of ad memorability and
overall user experience.
