Detecting Abnormal User Feedback Patterns through Temporal Sentiment Aggregation

TL;DR

This paper introduces a temporal sentiment aggregation method using transformer models to detect anomalies in user feedback over time, effectively identifying significant sentiment drops.

Contribution

It presents a novel framework combining pretrained transformers and temporal aggregation for detecting feedback anomalies, addressing noise and class imbalance issues.

Findings

Successfully detects statistically significant sentiment drops

Identifies coherent complaint patterns in social media data

Provides an interpretable approach for feedback anomaly monitoring

Abstract

In many real-world applications, such as customer feedback monitoring, brand reputation management, and product health tracking, understanding the temporal dynamics of user sentiment is crucial for early detection of anomalous events such as malicious review campaigns or sudden declines in user satisfaction. Traditional sentiment analysis methods focus on individual text classification, which is insufficient to capture collective behavioral shifts over time due to inherent noise and class imbalance in short user comments. In this work, we propose a temporal sentiment aggregation framework that leverages pretrained transformer-based language models to extract per-comment sentiment signals and aggregates them into time-window-level scores. Significant downward shifts in these aggregated scores are interpreted as potential anomalies in user feedback patterns. We adopt RoBERTa as our core semantic feature extractor and demonstrate, through empirical evaluation on real social media data, that the aggregated sentiment scores reveal meaningful trends and support effective anomaly detection. Experiments on real-world social media data demonstrate that our method successfully identifies statistically significant sentiment drops that correspond to coherent complaint patterns, providing an effective and interpretable solution for feedback anomaly monitoring.