  Advertisements are unavoidable in modern society. Times Square is notorious
for its incessant display of advertisements. Its popularity is worldwide and
smaller cities possess miniature versions of the display, such as Pittsburgh
and its digital works in Oakland on Forbes Avenue. Tokyo's Ginza district
recently rose to popularity due to its upscale shops and constant onslaught of
advertisements to pedestrians. Advertisements arise in other mediums as well.
For example, they help popular streaming services, such as Spotify, Hulu, and
Youtube TV gather significant streams of revenue to reduce the cost of monthly
subscriptions for consumers. Ads provide an additional source of money for
companies and entire industries to allocate resources toward alternative
business motives. They are attractive to companies and nearly unavoidable for
consumers. One challenge for advertisers is examining a advertisement's
effectiveness or usefulness in conveying a message to their targeted
demographics. Rather than constructing a single, static image of content, a
video advertisement possesses hundreds of frames of data with varying scenes,
actors, objects, and complexity. Therefore, measuring effectiveness of video
advertisements is important to impacting a billion-dollar industry. This paper
explores the combination of human-annotated features and common video
processing techniques to predict effectiveness ratings of advertisements
collected from Youtube. This task is seen as a binary (effective vs.
non-effective), four-way, and five-way machine learning classification task.
The first findings in terms of accuracy and inference on this dataset, as well
as some of the first ad research, on a small dataset are presented. Accuracies
of 84\%, 65\%, and 55\% are reached on the binary, four-way, and five-way tasks
respectively.
