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.
Abstract (translated)
在许多实际应用中，如客户反馈监控、品牌声誉管理和产品健康追踪，理解用户情感的时间动态对于早期检测异常事件（例如恶意刷评活动或用户满意度骤降）至关重要。传统情感分析方法侧重于单条文本分类，但因其固有的噪声和短用户评论中的类别不平衡问题，难以捕捉随时间变化的集体行为趋势。本研究提出一种时序情感聚合框架，该框架利用预训练的Transformer语言模型提取每条评论的情感信号，并将其聚合为时间窗口级别的评分。这些聚合评分的显著下降被解读为用户反馈模式中的潜在异常。我们采用RoBERTa作为核心语义特征提取器，并通过在真实社交媒体数据上的实证评估表明，聚合情感评分能揭示有意义的趋势并支持有效的异常检测。在真实社交媒体数据上的实验证明，我们的方法能够成功识别与连贯投诉模式对应的统计显著情感下降，为反馈异常监控提供了有效且可解释的解决方案。
URL
https://arxiv.org/abs/2604.00020
