TC-DAG and D-RoPE for Conversational Sentiment Analysis

A novel approach has been introduced that merges Thread-Constrained Directed Acyclic Graph (TC-DAG) with Discourse-Aware Rotary Position Embedding (D-RoPE) for analyzing conversational aspect-based sentiment quadruples (DiaASQ). Current techniques relying on basic Graph Convolutional Networks (GCN) tend to introduce structural noise and overlook the temporal sequences of dialogues, while conventional RoPE faces issues with Distance Dilution. The TC-DAG effectively mitigates cross-thread noise through thread constraints, ensures global connectivity via root anchoring, and incorporates temporal sequencing. Meanwhile, D-RoPE enhances the alignment of multi-layer semantic representations. This new method effectively overcomes challenges in understanding complex interrelationships within multi-turn dialogues.