Predicting Movie Trailer Viewer's “Like/Dislike” via Learned Shot Editing Patterns

Abstract

Nowadays, there are many movie trailers publicly available on social media website such as YouTube, and many thousands of users have independently indicated whether they like or dislike those trailers. Although it is understandable that there are multiple factors that could influence viewers’ like or dislike of the trailer, we aim to address a preference question in this work: Can subjective multimedia features be developed to predict the viewer's preference presented by like (by thumbs-up) or dislike (by thumbs-down) during and after watching movie trailers? We designed and implemented a computational framework that is composed of low-level multimedia feature extraction, feature screening and selection, and classification, and applied it to a collection of 725 movie trailers. Experimental results demonstrated that, among dozens of multimedia features, the single low-level multimedia feature of shot length variance is highly predictive of a viewer's “like/dislike” for a large portion of movie trailers. We interpret these findings such that variable shot lengths in a trailer tend to produce a rhythm that is likely to stimulate a viewer's positive preference. This conclusion was also proved by the repeatability experiments results using another 600 trailer videos and it was further interpreted by viewers'eye-tracking data.