Detecting Moral Features in TV Series with a Transformer Architecture through Dictionary-Based Word Embedding

Abstract

Moral features are essential components of TV series, helping the audience to engage with the story, exploring themes beyond sheer entertainment, reflecting current social issues, and leaving a long-lasting impact on the viewers. Their presence shows through the language employed in the plot description. Their detection helps regarding understanding the series writers’ underlying message. In this paper, we propose an approach to detect moral features in TV series. We rely on the Moral Foundations Theory (MFT) framework to classify moral features and use the associated MFT dictionary to identify the words expressing those features. Our approach combines that dictionary with word embedding and similarity analysis through a deep learning SBERT (Sentence-Bidirectional Encoder Representations from Transformers) architecture to quantify the comparative prominence of moral features. We validate the approach by applying it to the definition of the MFT moral feature labels as appearing in general authoritative dictionaries. We apply our technique to the summaries of a selection of TV series representative of several genres and relate the results to the actual content of each series, showing the consistency of results.