Machine learning methods fail to provide cohesive atheoretical construction of personality traits from semantic embeddings
This is an incremental study for psychology and computational linguistics, showing that machine learning may not replace established theories like the Big Five in personality modeling.
The study tackled the problem of constructing personality traits from semantic embeddings using machine learning, comparing a bottom-up model to the Big Five on one million Reddit comments, and found that the Big Five provided far more powerful and interpretable descriptions while the machine learning clusters failed to recover traits like Extraversion and lacked coherence.
The lexical hypothesis posits that personality traits are encoded in language and is foundational to models like the Big Five. We created a bottom-up personality model from a classic adjective list using machine learning and compared its descriptive utility against the Big Five by analyzing one million Reddit comments. The Big Five, particularly Agreeableness, Conscientiousness, and Neuroticism, provided a far more powerful and interpretable description of these online communities. In contrast, our machine-learning clusters provided no meaningful distinctions, failed to recover the Extraversion trait, and lacked the psychometric coherence of the Big Five. These results affirm the robustness of the Big Five and suggest personality's semantic structure is context-dependent. Our findings show that while machine learning can help check the ecological validity of established psychological theories, it may not be able to replace them.