Transformer-Based Personality Trait Recognition Enhanced by Contextual Augmentation

psychological research, it often suffers from label interference, vocabulary-driven overfitting, and limited labeled datasets. As a result, models are brittle: they can fail with small training samples and behave inconsistently across trait ranges. To address this, we employ a practical single-trait approach that uses five independent ELECTRA-based classifiers, each corresponding to one of the big five dimensions, and trained them as separate binary tasks to prevent cross-trait interference. To reduce lexical bias and double the Pennebaker and King essay corpus from ۲,۴۶۷ to ۴,۹۳۴ samples, the team applied careful synonym-replacement augmentation using WordNet and additionally incorporated contextual augmentation generated by the Gemma model. Models were adjusted methodically to ensure fair comparisons. With test AUCs above ۰.۷۵, the ensemble achieves an average test accuracy of ۰.۷۲۴ on the Pennebaker and King benchmark, with per-trait accuracies of ۰.۷۲, ۰.۷۱, ۰.۷۴, ۰.۷۳, and ۰.۷۲ for openness, conscientiousness, extraversion, agreeableness, and neuroticism (OCEAN), respectively. These results substantially reduce inter-trait interference while matching or surpassing LIWC baselines and other transformer approaches.

کلیدواژه ها:

Personality recognition ، Natural Language Processing ، Transformer Models ، Electra ، Big Five personality traits ، Computational Psychology