消费者投诉评估中的分布情感建模与异常检测
Distributional sentiment modeling and anomaly detection for consumer complaint assessment
AI总结：
本研究将消费者投诉负面情感视为有界连续变量，用Transformer评分和Beta分布建模，结合KL散度与Hellinger距离及金额、响应结果构建异常诊断，识别文本严重度与救济不一致的投诉，用于操作风险监控。
AI中文摘要：
情感分析是将非结构化文本转化为金融和风险管理中定量信号的常用工具。然而，大多数应用将输出简化为离散极性标签或单一预测特征，忽视了消费者投诉叙述中情感强度的分布结构。本文将消费者投诉中的负面情感视为有界连续变量，研究其完整分布而非单一标签。我们使用Transformer分类器对每条叙述进行评分，用Beta分布对评分建模，并通过Kullback-Leibler散度和平方Hellinger距离比较有理由投诉与无理由投诉的拟合分布。随后，将拟合分布与美元金额和公司响应结果关联，构建异常诊断指标，以标记文本严重程度与记录救济不一致的投诉。我们发现这两组投诉的分布高度重叠，因此负面情感强度本身并非结果的尖锐分类器；结合货币和类别属性，它可以识别出异常严重的投诉，用于操作风险监控。本研究将情感分析视为连续分布测量，在消费者投诉评估的统一框架中整合了情感提取、有界响应建模和异常检测。
英文摘要：
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.
