用变分框架分离情绪特征，提升政治立场识别效果
Disentangled VAD Representations via a Variational Framework for Political Stance Detection
- 通过变分自编码器分离情绪的三维度（价值、唤醒、支配）
- 在P-STANCE和SemEval-2016上超越BERT、GPT-4o等模型
- 适合需要精细情绪理解的社交媒体分析任务
立场检测旨在对特定议题判断立场。现有方法难以有效融合情感信息，且忽视细粒度情感标注的作用。本文提出基于变分自编码器（VAE）的新型立场检测框架，从社交媒体政治话语中解耦出价值（Value）、唤醒（Arousal）和支配（Dominance）三维度情绪特征。该方法在目标内与跨目标场景下均优于现有模型。研究采用先进情感标注工具为P-STANCE数据集标注七类情感标签。在P-STANCE和SemEval-2016等基准数据集上的评估显示，PoliStance-VAE性能达到当前最优，超越BERT、BERTweet和GPT-4o。该框架提供了一种鲁棒且可解释的解决方案，验证了融入细微情绪表征的有效性，为需深度情绪理解的自然语言处理任务开辟新路径。
原文摘要 · Abstract (English)
The stance detection task aims to categorise the stance regarding specified targets. Current methods face challenges in effectively integrating sentiment information for stance detection. Moreover, the role of highly granular sentiment labelling in stance detection has been largely overlooked. This study presents a novel stance detection framework utilizing a variational autoencoder (VAE) to disentangle latent emotional features-value, arousal, and dominance (VAD)-from political discourse on social media. This approach addresses limitations in current methods, particularly in in-target and cross-target stance detection scenarios. This research uses an advanced emotional annotation tool to annotate seven-class sentiment labels for P-STANCE. Evaluations on benchmark datasets, including P-STANCE and SemEval-2016, reveal that PoliStance-VAE achieves state-of-the-art performance, surpassing models like BERT, BERTweet, and GPT-4o. PoliStance-VAE offers a robust and interpretable solution for stance detection, demonstrating the effectiveness of integrating nuanced emotional representations. This framework paves the way for advancements in natural language processing tasks, particularly those requiring detailed emotional understanding.
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