This WASSA 2022 paper studies human-personality prediction from text; it does not assess an LLM's personality or show that BERT has traits of its own. It uses two benchmarks. Essays contains 2,468 student essays averaging 672 words with five binary labels derived from Big Five self-report. Kaggle MBTI aggregates PersonalityCafe posts from 8,675 users, averaging 1,288 words per user, with four self-reported MBTI dichotomies. The system extracts what it calls 437 sentence-level psycholinguistic features and retains their sequence or 'text contour': morphosyntactic complexity, lexical richness, readability, and sentiment/emotion/affect signals. The four reported groups actually sum to 436 (19+77+14+326), and the equations likewise use 436-dimensional vectors. The paper compares BLSTMs with and without attention, late-fusion combinations with `bert-base-uncased`, frozen versus fine-tuned BERT, and a stacked ensemble of ten instances. BERT sees only the first 512 tokens, while the contours represent document sentences. The paper reports grid search and ten repetitions of 10-fold cross-validation, but it does not document folds, seeds, the search grid, checkpoint selection, or an outer/nested evaluation for the stacking stage. The main table reports accuracy only. The ensemble averages 63.50% on Essays and 86.51% on MBTI. On Essays, this is 2.90 percentage points above the best displayed prior average of 60.6%. On MBTI, 86.51 minus 77.1 equals 9.41 points, not the 8.28% repeated in the abstract and results; 8.28 points corresponds to the best single fine-tuned model, 85.38%, relative to 77.1. Without BERT, ATTN-PSYLING reaches 60.04% and 75.29%. SP-LIME ranks sentiment/emotion/affect first for all nine targets and lexical features second except for P/J. This is a post-hoc explanation created by zeroing feature groups, not a retraining ablation or causal evidence that the cues express personality. Accuracy is particularly hard to interpret for MBTI because the paper's own figure shows class imbalance and it reports no balanced accuracy, macro-F1, intervals, or variability. It also does not control whether forum texts mention MBTI labels or associated vocabulary. There is no cross-domain validation, demographic analysis, bias audit, or consequence evaluation. Neither ACL nor the paper links code, and targeted searches found no implementation. Without extracted features, predictions, folds, and complete configurations, the results cannot be reproduced end to end. The defensible conclusion is narrow: on these two datasets and under a protocol that is not fully auditable, combining BERT representations with sequences of psycholinguistic measurements yields higher accuracy than the cited baselines; it does not establish a general, fair, or causal measurement of personality.
Research question
Does textual classification of Big Five and MBTI labels improve by representing the phrase-by-phrase distribution of psycholinguistic features and combining those contours with BERT, and which feature groups contribute to the predictions?