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Depression affects emotional expression and perception. As a non-invasive and privacy-preserving method, speech is widely used for automatic depression detection. However, existing models often focus only on depressive features in speech, ignoring the differential emotion expression patterns across different emotion-inducing tasks. To address this, we propose an emotion-guided graph attention network (emoGAT) for depression detection. By collecting speech-text data from depressed individuals and healthy controls during emotion-inducing tasks, we construct graph embeddings using sentiment cues from both speech and text. Experimental results show our method reduces the standard deviation by 1.8% and improves accuracy by 4.36%. Graph attention visualization also reveals depression-specific characteristics, such as flattened prosody in neutral picture description tasks and cognitive biases toward negative information, offering deeper insights into emotional relational expressions.