情感分析是自然语言处理中的一项核心任务，主要评估文本中表达的情绪或感情色彩.在当前的情感分析研究中，多数模型均依赖于双向变换器模型（BERT）作为特征提取器，且主要聚焦于较为简单的二分类或三分类任务.针对细粒度情感分析，提出了一种新的混合双通道门控循环单元和卷积神经网络（GRU-CNN）情感分析模型（GGC）.该模型利用生成式预训练变换器（GPT）作为特征提取器，更精准地捕获文本中的深层含义.在此基础上，模型将提取到的文本特征输入到多通道的GRU和CNN中，分别捕获全局和局部特征.同时该模型引入了注意力机制，将这2种特征进行动态融合.模型根据不同特征重要性，分配差异化权重，聚焦关键情感信息.结果表明，该方法在情感分析任务中展现出显著优势.

Sentiment analysis is a core task in natural language processing， involving the assessment of emotions or sentiment expressed within texts. In the current research on sentiment analysis， most models rely on bidirectional encoder representations from transformers （BERT） as a feature extractor， focusing mainly on relatively simple binary or ternary tasks. To address fine - grained sentiment classification， the paper introduces a new hybrid dual-channel gated recurrent unit and convolutional neural network （GRU - CNN） sentiment analysis model（GGC）. This model uses generative pre-trained transformer （GPT） as a feature extractor， capturing the deeper meanings in the text more precisely. Based on this， the text features extracted are fed into multi - channel GRU and CNN， capturing both global and local features respectively. The model also incorporates an attention mechanism， which dynamically fuses these two types of features. This mechanism allows the model to allocate different weights to different parts according to their importance， thus capturing key emotional information in the text more accurately. Experimental results show that this method achieves excellent performance in sentiment analysis tasks.