Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2504.15848v1
• Date: Tue, 22 Apr 2025 12:43:37 GMT
• Title: Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis
• Authors: Luwei Xiao, Rui Mao, Shuai Zhao, Qika Lin, Yanhao Jia, Liang He, Erik Cambria,
• Abstract summary: Multimodal aspect-based sentiment classification (MASC) is an emerging task due to an increase in user-generated multimodal content on social platforms.<n>Despite extensive efforts and significant achievements in existing MASC, substantial gaps remain in understanding fine-grained visual content.<n>We present Chimera: a cognitive and aesthetic sentiment causality understanding framework to derive fine-grained holistic features of aspects.
• Score: 34.100793905255955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal aspect-based sentiment classification (MASC) is an emerging task due to an increase in user-generated multimodal content on social platforms, aimed at predicting sentiment polarity toward specific aspect targets (i.e., entities or attributes explicitly mentioned in text-image pairs). Despite extensive efforts and significant achievements in existing MASC, substantial gaps remain in understanding fine-grained visual content and the cognitive rationales derived from semantic content and impressions (cognitive interpretations of emotions evoked by image content). In this study, we present Chimera: a cognitive and aesthetic sentiment causality understanding framework to derive fine-grained holistic features of aspects and infer the fundamental drivers of sentiment expression from both semantic perspectives and affective-cognitive resonance (the synergistic effect between emotional responses and cognitive interpretations). Specifically, this framework first incorporates visual patch features for patch-word alignment. Meanwhile, it extracts coarse-grained visual features (e.g., overall image representation) and fine-grained visual regions (e.g., aspect-related regions) and translates them into corresponding textual descriptions (e.g., facial, aesthetic). Finally, we leverage the sentimental causes and impressions generated by a large language model (LLM) to enhance the model's awareness of sentimental cues evoked by semantic content and affective-cognitive resonance. Experimental results on standard MASC datasets demonstrate the effectiveness of the proposed model, which also exhibits greater flexibility to MASC compared to LLMs such as GPT-4o. We have publicly released the complete implementation and dataset at https://github.com/Xillv/Chimera

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