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Detecting Abnormal User Feedback Patterns through Temporal Sentiment Aggregation

Yalun Qi, Sichen Zhao, Zhiming Xue, Xianling Zeng, Zihan Yu

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.

Detecting Abnormal User Feedback Patterns through Temporal Sentiment Aggregation

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.

Paper Structure

This paper contains 32 sections, 9 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Sentiment Analysis Frameworks
  4. Sentiment-Based Anomaly Detection
  5. Deep Learning for Time-Series Anomaly Detection
  6. Objective Alignment and Representation Robustness
  7. Problem Formulation
  8. Methodology
  9. RoBERTa as a Semantic Feature Extractor
  10. Temporal Aggregation Framework
  11. Topic-Aware Sentiment Aggregation
  12. Anomaly Detection Mechanism
  13. Implementation
  14. System Architecture
  15. Data Preprocessing
  16. ...and 17 more sections

Figures (5)

  • Figure 1: Temporal aggregated sentiment trajectory. Red markers indicate detected anomaly windows where significant sentiment drops occur.
  • Figure 2: Temporal sentiment change ($\Delta$Score) with anomaly detection threshold $\tau = -0.1693$. Downward spikes crossing the threshold correspond to abnormal sentiment deterioration.
  • Figure 3: Comparison of negative complaint reason distributions between anomalous and normal windows. Anomalous windows exhibit stronger concentration of delay and service-related complaint categories.
  • Figure 4: Topic-wise sentiment trajectories across time windows. Each curve represents the aggregated sentiment trend for a specific complaint category.
  • Figure 5: Topic-wise sentiment heatmap over time windows. Colors represent aggregated sentiment scores for each complaint topic.