ANEST Narrative–Affect Dataset (ANAD v1): Large-Scale Narrative–Affect Discrepancy Corpus for Computational Psychology and Emotional AI

The ANEST Narrative–Affect Dataset (ANAD v1) is a large-scale, fully curated corpus designed to measure narrative–affect discrepancy in human-generated texts. Developed at the Ryan Research Institute (RRI), Paris, the dataset integrates narrative complexity metrics, affective polarity scores, and a novel discrepancy index—NADI_abs—proposed as a computational indicator of emotional incongruence within narratives. All narratives in ANAD v1 were collected from the r/relationships subreddit using the Pushshift API and the official Reddit API (2012–2023), then cleaned and anonymized at RRI. Personally identifying information was removed or masked, and only derived features are used in the main analyses. Earlier public snapshots of the same community (e.g., reddit_small.csv, reddit_small_clean.parquet) are included in this record solely as provenance and for historical comparison; the primary analytic dataset is provided as reddit_narrative_affect_v1.parquet. ANAD v1 contains three major components: (1) Full narrative–affect dataset 351,734 Reddit narratives (after cleaning and filtering) Narrative metrics: Length-of-Context (LoC), structural complexity, narrative density Affective metrics: VADER-derived sentiment polarity (normalized), affect variance Computed discrepancy index: NADI_abs = |LoC − sentiment_norm| (0–10 continuous scale) Cleaned and standardized in Parquet format (reddit_narrative_affect_v1.parquet) for high-performance processing (2) Statistical and diagnostic documentation Summary statistics (summary_stats_v1.csv) Correlation matrix across narrative and affective variables (corr_matrix_v1.csv) High-NADI and low-NADI qualitative samples (high_NADI_samples_v1.csv, low_NADI_samples_v1.csv) Group-level comparisons for high vs. low NADI_abs (group_stats_high_low_NADI_v1.csv) Histogram bin tables for reproducible visualizations (nadi_hist_bins_v1.csv, loc_sentiment_sample_v1.csv, loc_sentiment_sample_large_v1.csv) (3) Reproducibility and metadata files Dataset schema (dataset_schema_v1.json) CHANGELOG and version history (CHANGELOG.md) Full preprocessing and scoring pipeline (anest_nadi_pipeline_v1.ipynb) CC-BY 4.0 License and citation file (LICENSE, CITATION.cff) README with usage notes and ethical guidelines (README.md) ANAD v1 represents the first public dataset created explicitly to study narrative–affect inconsistency as a measurable psychological construct. It is designed for research in computational psychology, affective computing, mental health modeling, and Emotional AI systems. The dataset supports reproducible experimentation on emotional incongruence, narrative coherence, affective drift, and algorithmic affect interpretation. In this dataset release, the discrepancy index is provided as the simple absolute-difference measure NADI_abs = |LoC − sentiment_norm|, which is intuitive and easy to reuse across settings. A more advanced, residual-based variant of the Narrative–Affect Discrepancy Index—defined via generalized additive models and used to model the geometry of the narrative–affect space—is introduced in the companion article “The Great Narrative–Affect Gap” (Kim, under review). That residual-based NADI can be derived from ANAD v1 when needed, using the public pipeline and documentation provided here. This work is part of the broader ANEST (Affective Neurocomputational Storytelling) program at RRI, which investigates emotional reasoning, predictive selfhood, and affective sovereignty in artificial and human systems.