基于门控噪声过滤与情感相关性交互的多模态方面级情感分析
Multimodal Aspect-Level Sentiment Analysis Based on Gated Noise Filtering and Emotion-Relevance Interaction
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中文总结 AI 辅助
针对多模态方面级情感分析中的噪声干扰和跨模态对齐弱问题，提出GNSRI框架，通过门控噪声过滤和情感相关性交互提升性能，在基准上准确率提升约2%。
中文摘要 AI 辅助
多模态基于方面的情感分析（MABSA）通过联合建模文本和图像，推断针对特定方面的细粒度情感极性。尽管跨模态融合取得了进展，但在多方面的场景中仍存在两个挑战：（1）多模态噪声，即与方面无关的内容会干扰情感学习；（2）跨模态情感对齐较弱，因为视觉证据可能模糊不清，且文本与视觉情感可能冲突，限制了多模态的互补性。为解决这些问题，我们提出了一个门控噪声过滤的情感相关性交互（GNSRI）框架。该框架采用门控噪声过滤模块来抑制与情感无关的特征并增强方面感知的情感线索，以及一个情感相关性交互模块来在微观和宏观层面捕获一致和冲突的跨模态信号。最后，一种可学习的决策融合机制在方面级别自适应地结合来自文本、视觉和跨模态分支的预测。在公开的MABSA基准上的实验表明，GNSRI优于最先进的方法，在Twitter-2015和Twitter-2017上分别将准确率提高了1.94%和2.06%。
英文摘要
Multimodal Aspect-Based Sentiment Analysis (MABSA) infers fine-grained sentiment polarity toward specific aspects by jointly modeling text and images. Despite progress in cross-modal fusion, two challenges remain in multi-aspect settings: (1) multimodal noise, where aspect-irrelevant content distracts sentiment learning; and (2) weak cross-modal sentiment alignment, as visual evidence can be ambiguous and textual--visual sentiments may conflict, limiting multimodal complementarity. To address these issues, we propose a Gated Noise-filtered Sentiment-Relevance Interaction (GNSRI) framework. It employs a gated noise-filtering module to suppress sentiment-irrelevant features and enhance aspect-aware sentiment cues, and a sentiment-relevance interaction module to capture consistent and conflicting cross-modal signals at micro and macro levels. Finally, a learnable decision fusion mechanism adaptively combines predictions from textual, visual, and cross-modal branches at the aspect level. Experiments on public MABSA benchmarks show that GNSRI outperforms state-of-the-art methods, improving accuracy by 1.94\% and 2.06\% on Twitter-2015 and Twitter-2017, respectively.
发表机构
- Hubei University（湖北大学）
- Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education（教育部智能感知系统与安全重点实验室（湖北大学））
- Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University)（湖北省大数据智能分析与应用重点实验室（湖北大学））
机构由 AI 辅助整理，请以论文原文为准。
