MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction

Opinions and attitudes towards controversial social and political issues are hardly ever based on evidence alone. Moral values play a fundamental role in the decision-making process of how we perceive and interpret information. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on WordNet synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increasing complexity, ranging from statistical properties of the lexicon to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (p-value<0.01). Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues.