Dimensions overall score 7.0

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• The proposed Zero-Initialized Residual Complex Projection (ZRCP) and Anti-collision Masked Angle Loss framework achieves a state-of-the-art Macro-F1 score of 0.8851 on the ASAP dataset.result 95%
• The anti-collision mask expands the inter-polarity discriminative margin by over 50% compared to standard contrastive learning.result 90%
• Projecting textual features into a complex semantic space using phase to disentangle sentiment polarities and amplitude to encode semantic intensity is more effective than real-valued embeddings for ABSA.method 85%
• Standard contrastive learning suffers from false-negative collisions that severely degrade performance on high-frequency aspects in ABSA.limitation 80%
• Explicitly penalizing the complex amplitude catastrophically over-regularizes subjective representations in sentiment analysis.technical 75%
• The Zero-Initialized Residual Complex Projection acts as an identity mapping at training start, ensuring safe lower-bound performance before learning feature disentanglement.method 85%
• The phase-driven angle objective maintains constant gradient for disentangling hard negatives, unlike cosine similarity whose gradient vanishes in high-density clusters.technical 80%
• Removing the anti-collision mask leads to feature entanglement among high-frequency aspects in ABSA.result 80%

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