EmoAtlas: An emotional profiling tool merging psychological lexicons, artificial intelligence and network science

Abstract This work introduces EmoAtlas as a computational framework extracting emotions and syntactic/semantic word associations automatically from texts. EmoAtlas implements a cutting-edge synergy of interpretable artificial intelligence (AI) and psychologically-validated lexicons. Our framework supports out-of-the-box emotion detection of 8 categorical emotions and syntactic network building in 18 languages. In the task of detecting emotions in human-labelled social media posts and news media articles, either in Italian or in English, EmoAtlas achieves performances analogous, or even superior (e.g., 70.2% precision for detecting joy in tweets), to state-of-art natural language processing techniques like BERT, RoBERTa, distillBERT and ALBERT (e.g., 67.9% precision for detecting joy in tweets). This performance comes with EmoAtlas being 12 times faster than BERT. In a psychometric task like reproducing human creativity ratings for 1,071 short stories, EmoAtlas and BERT obtain equivalent predictive power (ρ ≈0.495, p<10-4). Combining BERT's semantic features with EmoAtlas' emotional/syntactic networks of words, the cross-validated resulting AIs get substantially better at estimating creativity rates of stories (ρ ≈0.628, p<10-4). This indicates an interplay between the creativity of narratives and the semantic, emotional, and syntactic divergence of their words, pointing out how EmoAtlas and BERT-like models could be used in synergy in psychometrics. By outputting interpretable emotional profiles and syntactic networks, EmoAtlas can quantify how emotions are channelled through specific associations in texts, e.g., how did customers frame their ideas and emotions towards "beds" in hotel reviews? We release EmoAtlas as a standalone computational tool and discuss its impact in modelling texts as data, promisingly filling current research gaps in AI and cognitive science.