Automatic assignment of moral foundations to movies by word embedding

Abstract

Morality is a topic that people are increasingly concerned about. Morality is observed and measured during public acts or when developing and consuming products, such as movies. The Moral Foundations Theory (MFT) was developed to rigorously perform these measurements with the support of the Moral Foundations Dictionary (MFD). In this paper, a Word Embedding-based Moral Foundation Assignment (WEMFA) approach has been designed, implemented, and applied to the movie domain for multiple assignment of moral foundations. WEMFA may use any dictionary, and it has been applied to a movie collection generated from movie synopses. A comparison between WEMFA and MoralStrength, the only approach found in the scientific literature, has been carried out. The proposed approach provided a percentage improvement of 41.7% with respect to the best version of MoralStrength, which uses an extension of the original MFD almost 10 times larger in number of terms. In addition, an extension of the original MFD (MFD24) has been built by adding 14 new moral foundations to the 10 original ones, enriching the moral context. WEMFA provided a mean accuracy of 78% with MFD24 despite the increment of the number of moral foundations. Besides, new extended dictionaries or even totally different ones can be used with WEMFA, since it does not need any training.