# Distributional sentiment modeling and anomaly detection for consumer complaint assessment

arXiv: https://arxiv.org/abs/2609.31653 · HTML: https://mlsanity.com/paper/2609.31653/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024)
- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2409.01980/) (2024)
- [Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2405.05496/) (2024)
- [Comparison of Topic Modelling Approaches in the Banking Context](https://mlsanity.com/paper/2402.03176/) (2024)
- [RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data](https://mlsanity.com/paper/2404.07452/) (2024)
- [Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2407.00341/) (2024)
- [Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2412.12564/) (2024)
- [NLP-based detection of systematic anomalies among the narratives of consumer complaints](https://mlsanity.com/paper/2308.11138/) (2023)
- [Log-concave Density Estimation with Independent Components](https://mlsanity.com/paper/2401.01500/) (2024)
- [Scalable and robust regression models for continuous proportional data](https://mlsanity.com/paper/2504.15269/) (2025)
- [Policy Gradient-Driven Noise Mask](https://mlsanity.com/paper/2406.14568/) (2024)
- [Shrinkage Estimators for Beta Regression Models](https://mlsanity.com/paper/2406.18047/) (2024)
- [Extended-support beta regression for [0, 1] responses](https://mlsanity.com/paper/2409.07233/) (2024)
- [HELLINGER-UCB: A novel algorithm for stochastic multi-armed bandit problem and cold start problem in recommender system](https://mlsanity.com/paper/2404.10207/) (2024)
- [A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series](https://mlsanity.com/paper/2405.03047/) (2024)
- [Generative Sentiment Analysis via Latent Category Distribution and Constrained Decoding](https://mlsanity.com/paper/2407.21560/) (2024)
- [Bayesian Optimization over Bounded Domains with the Beta Product Kernel](https://mlsanity.com/paper/2506.16316/) (2025)
- [From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints](https://mlsanity.com/paper/2606.22664/) (2026)

## Abstract (arXiv)

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.