A New Way of Making Advertising Copies: Image as Input

Abstract

Our impression can be effectively delivered by a color. In this paper we present a novel model for generating advertising copies using machine learning techniques. Unlike most of the previously reported advertising copy generators take specified keyword(s) which a user wants to embed in a copy, our proposed model takes one colored image as its input. We use the previously reported database that provides the potential color impression of a word for the purpose of selecting several words assumed to give a similar perceptual impression of the input image. We also use a deep neural network based binary classifier to extract appropriate words for advertising copies from an increased vocabulary. To output advertising copies of relatively natural expression out of the ones generated, we use a word embedding model of a shallow neural network called Skip-gram. The qualities of the advertising copies were evaluated by online survey and were compared with other copies generated by various models. As the result of the evaluation, our proposed model outperformed the other models.