SIMPA: Statement-to-Item Matching Personality Assessment from text

Automated text-based personality assessment (ATBPA) methods can analyze large amounts of text data and identify nuanced linguistic cues. However, current approaches lack the interpretability, explainability, validity offered by standard questionnaire instruments. To address these weaknesses, we propose an approach that combines questionnaire-based to assessment. Our Statement-to-Item Matching Personality Assessment (SIMPA) framework uses natural language processing detect self-referencing descriptions in a target’s utilizes for The core is notion trait-constrained semantic similarity between freely expressed statements items. conceptual basis provided realistic accuracy model (RAM), which describes process accurate judgments extend with feedback loop mechanism improve judgments. We present simple proof-of-concept implementation SIMPA ATBPA on social media site Reddit. show how be used directly unsupervised estimation Big 5 scores indirectly produce features supervised model, demonstrating state-of-the-art results prediction task