Multimodal Aspect-Level Sentiment Analysis Based on Gated Noise Filtering and Emotion-Relevance Interaction

2026-09-16

AI Summary

为解决多模态情感分析中的噪声干扰和跨模态情感对齐问题，提出了一种基于门控噪声过滤和情感相关性交互的框架GNSRI。

Abstract

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.

Problem

• Multimodal Aspect-Based Sentiment Analysis
• multimodal noise
• cross-modal sentiment alignment

Innovation

• Gated Noise-Filtered Sentiment-Relevance Interaction
• Cross-Modal Fusion
• Sentiment Analysis