Is Einstein more agreeable and less neurotic than Hitler? A computational exploration of the emotional and personality profiles of historical persons

TL;DR

This study uses advanced semantic models to estimate and compare the personality and emotional profiles of 100 notable historical figures across arts, politics, and science, highlighting the potential and limitations of computational personality analysis.

Contribution

It extends existing algorithms to analyze personality traits of historical figures using static and dynamic language models, demonstrating their application and limitations.

Findings

Both static and dynamic models can estimate personality traits.

Models show potential but have limitations in accuracy.

The approach offers a new tool for psychology and data science.

Abstract

Recent progress in distributed semantic models (DSM) offers new ways to estimate personality traits of both fictive and real people. In this exploratory study we applied an extended version of the algorithm developed in Jacobs (2019) to compute the likeability scores, emotional figure profiles and BIG5 personality traits for 100 historical persons from the arts, politics or science domains whose names are rather unique (e.g., Einstein, Kahlo, Picasso). We compared the results produced by static (word2vec) and dynamic (BERT) language model representations in four studies. The results show both the potential and limitations of such DSM-based computations of personality profiles and point ways to further develop this approach to become a useful tool in data science, psychology or computational and neurocognitive poetics (Jacobs, 2015).