Distributional sentiment modeling and anomaly detection for consumer complaint assessment
Abstract
Sentiment analysis is a common tool for converting unstructured text into quantitative signals in finance and risk management. Yet most applications reduce the output to a discrete polarity label or a single predictive feature, overlooking the distributional structure of sentiment intensity in consumer complaint narratives. In this paper we treat negative sentiment in consumer complaints as a bounded continuous variable and study its full distribution rather than a single label. We score each narrative with a transformer classifier, model the scores with Beta distributions, and compare the fitted distributions of meritorious and non-meritorious complaints through the Kullback Leibler divergence and the squared Hellinger distance. The fitted distributions are then linked with dollar amounts and company response outcomes to construct anomaly diagnostics that flag complaints whose textual severity is inconsistent with the recorded relief. We find that the two groups have strongly overlapping distributions, so negative sentiment intensity is not a sharp classifier of outcomes on its own; combined with monetary and categorical attributes, it isolates unusually severe complaints for operational risk monitoring. Treating sentiment analysis as continuous distributional measurement, this study links sentiment extraction, bounded response modeling, and anomaly detection in a unified framework for consumer complaint assessment.
Related
- Statistical Foundations for a Google Play User-Review Sentiment Index: Signal Fusion, Shrinkage, Distributional Validation, and Dynamic Smoothing
- Fear Moves Markets: Sentiment-Augmented POMP for Volatility Modeling of Bitcoin Returns
- Online detection of distributional changes for time series in metric spaces
- On The Relevance Of Using Temperature For Mechanical Fault Detection In Industrial Robots Based On Dynamic Modeling
- Fundamental of Modeling for the Assessment of Organizational Risks in the Integrated Safety System at Explosion and Fire-Hazardous Enterprises
BibTeX
@misc{gao2026distributional,
title = {{Distributional sentiment modeling and anomaly detection for consumer complaint assessment}},
author = {Peiheng Gao and Chen Yang and Shimin Zhang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.31653},
eprint = {2609.31653},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.31653},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Distributional sentiment modeling and anomaly detection for consumer complaint assessment
AU - Peiheng Gao
AU - Chen Yang
AU - Shimin Zhang
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.31653
UR - https://arxiv.org/abs/2609.31653
AB - Sentiment analysis is a common tool for converting unstructured text into quantitative signals in finance and risk management. Yet most applications reduce the output to a discrete polarity label or a single predictive feature, overlooking the distributional structure of sentiment intensity in consumer complaint narratives. In this paper we treat negative sentiment in consumer complaints as a bounded continuous variable and study its full distribution rather than a single label. We score each narrative with a transformer classifier, model the scores with Beta distributions, and compare the fitted distributions of meritorious and non-meritorious complaints through the Kullback Leibler divergence and the squared Hellinger distance. The fitted distributions are then linked with dollar amounts and company response outcomes to construct anomaly diagnostics that flag complaints whose textual severity is inconsistent with the recorded relief. We find that the two groups have strongly overlapping distributions, so negative sentiment intensity is not a sharp classifier of outcomes on its own; combined with monetary and categorical attributes, it isolates unusually severe complaints for operational risk monitoring. Treating sentiment analysis as continuous distributional measurement, this study links sentiment extraction, bounded response modeling, and anomaly detection in a unified framework for consumer complaint assessment.
ER -