Prediction of instantaneous likeability of advertisements using deep learning

Abstract

The degree to which advertisements are successful is of prime concern for vendors in highly competitive global markets. Given the astounding growth of multimedia content on the internet, online marketing has become another form of advertising. Researchers consider advertisement likeability a major predictor of effective market penetration. An algorithm is presented to predict how much an advertisement clip will be liked with the aid of an end-to-end audiovisual feature extraction process using cognitive computing technology. Specifically, the usefulness of different spatial and time-domain deeplearning architectures such as convolutional neural and long short-term memory networks is investigated to predict the frame-by-frame instantaneous and root mean square likeability of advertisement clips. A data set named the 'BUET Advertisement Likeness Data Set', containing annotations of frame-wise likeability scores for various categories of advertisements, is also introduced. Experiments with the developed database show that the proposed algorithm performs better than existing methods in terms of commonly used performance indices at the expense of slightly increased computational complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

What Likeability Attributes Attract People to Watch Online Video Advertisements?

“…The study found that entertainment, informativeness, and customization were the strongest positive drivers of purchase intention, and irritation negatively influenced purchase intention. To predict the instantaneous likeability of advertisements, Saha et al [29] proposed a prediction algorithm based on deep learning and compared it with other methods. The authors classified advertisement clips into five categories (i.e., entertaining, creative, emotional, humorous, and miscellaneous) based on the overall impression of raters.…”

“…In addition, most previous studies have used perception data through survey methods. Saha et al [29] proposed a prediction algorithm based on deep learning methods to predict the instantaneous likeability of an advertisement. However, they did not classify the advertisement into likeability factors from the perspective of viewers and validate them.…”