标题： 主观与客观QoE的桥梁：基于LLM评论分析和网络MOS比较的运营商级聚合 显示英文标题

标题： Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison

摘要： 本文介绍了一种双层框架，用于网络运营商端的体验质量（QoE）评估，该框架结合了客观的网络建模和从直播平台提取的主观用户感知。 在客观方面，我们开发了一个机器学习模型，该模型基于通过ITU-T P.1203参考实现计算出的平均意见分数（MOS），仅使用网络参数如丢包率、延迟、抖动和吞吐量即可准确预测用户感知的视频质量，而无需依赖视频内容或客户端设备的仪器。 在主观方面，我们提出了一种语义过滤和评分流程，对直播中的用户评论进行处理以提取与性能相关的反馈。 使用大型语言模型以确定性和可重复的方式为过滤后的评论分配标量MOS分数。 为了支持可扩展且可解释的分析，我们构建了一个包含47,894条直播评论的标记数据集，其中约34,000条评论通过多层语义过滤被识别为与QoE相关。 每条评论都附加了模拟的互联网服务提供商归属信息，并使用合成时间戳在5分钟间隔内进行时间对齐。 该数据集使得可以在运营商层面进行用户感知质量的聚合和时间序列分析。 提出了一种delta MOS指标，用于测量每个互联网服务提供商与平台整体情感的偏差，即使在没有直接网络遥测的情况下也能检测到局部退化。 受控停机模拟验证了该框架在仅通过评论趋势识别服务中断方面的有效性。 该系统为每个运营商提供其在每个时间段的主观MOS以及全局平台平均值，从而实现实时解释性能偏差并与基于客观网络的QoE估计进行比较。 显示英文摘要

摘要： This paper introduces a dual-layer framework for network operator-side quality of experience (QoE) assessment that integrates both objective network modeling and subjective user perception extracted from live-streaming platforms. On the objective side, we develop a machine learning model trained on mean opinion scores (MOS) computed via the ITU-T P.1203 reference implementation, allowing accurate prediction of user-perceived video quality using only network parameters such as packet loss, delay, jitter, and throughput without reliance on video content or client-side instrumentation. On the subjective side, we present a semantic filtering and scoring pipeline that processes user comments from live streams to extract performance-related feedback. A large language model is used to assign scalar MOS scores to filtered comments in a deterministic and reproducible manner. To support scalable and interpretable analysis, we construct a labeled dataset of 47,894 live-stream comments, of which about 34,000 are identified as QoE-relevant through multi-layer semantic filtering. Each comment is enriched with simulated Internet Service Provider attribution and temporally aligned using synthetic timestamps in 5-min intervals. The resulting dataset enables operator-level aggregation and time-series analysis of user-perceived quality. A delta MOS metric is proposed to measure each Internet service provider's deviation from platform-wide sentiment, allowing detection of localized degradations even in the absence of direct network telemetry. A controlled outage simulation confirms the framework's effectiveness in identifying service disruptions through comment-based trends alone. The system provides each operator with its own subjective MOS and the global platform average per interval, enabling real-time interpretation of performance deviations and comparison with objective network-based QoE estimates.