融合CNN与GRU分析股市情绪，实现风险预警
Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
- 用CNN提取文本局部特征，GRU捕捉情绪演变时序
- 模型能识别长期依赖，提升风险预警准确率
- 适合金融风控、量化交易等场景使用
本文通过整合卷积神经网络（CNN）与门控循环单元（GRU），深入分析股票市场情绪，实现精准风险预警。利用CNN强大的特征提取能力，对海量网络文本数据进行预处理与分析，识别局部特征与模式；提取的特征序列随后输入GRU模型，以理解情绪状态随时间的演变及其对未来市场情绪和风险的潜在影响。该方法有效应对时间序列数据中的顺序依赖与长期依赖问题，实现了对股票市场情绪的细致分析，并构建了有效的早期风险预警系统。
原文摘要 · Abstract (English)
This document presents an in-depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling precise risk alerts. The robust feature extraction capability of CNN is utilized to preprocess and analyze extensive network text data, identifying local features and patterns. The extracted feature sequences are then input into the GRU model to understand the progression of emotional states over time and their potential impact on future market sentiment and risk. This approach addresses the order dependence and long-term dependencies inherent in time series data, resulting in a detailed analysis of stock market sentiment and effective early warnings of future risks.
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