  In order to resonate with the viewers, many video advertisements explore
creative narrative techniques such as "Freytag's pyramid" where a story begins
with exposition, followed by rising action, then climax, concluding with
denouement. In the dramatic structure of ads in particular, climax depends on
changes in sentiment. We dedicate our study to understand the dynamic structure
of video ads automatically. To achieve this, we first crowdsource climax
annotations on 1,149 videos from the Video Ads Dataset, which already provides
sentiment annotations. We then use both unsupervised and supervised methods to
predict the climax. Based on the predicted peak, the low-level visual and audio
cues, and semantically meaningful context features, we build a sentiment
prediction model that outperforms the current state-of-the-art model of
sentiment prediction in video ads by 25%. In our ablation study, we show that
using our context features, and modeling dynamics with an LSTM, are both
crucial factors for improved performance.
