AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis

TL;DR

AdaptiSent is a novel framework for multimodal aspect-based sentiment analysis that employs adaptive cross-modal attention to dynamically focus on relevant textual and visual cues, significantly improving accuracy.

Contribution

It introduces a context-aware adaptive attention mechanism for MABSA, enhancing sentiment classification and aspect extraction by better integrating multimodal information.

Findings

Outperforms baseline models in precision, recall, and F1 score on Twitter datasets.

Effectively captures nuanced inter-modal relationships for improved sentiment analysis.

Demonstrates superior performance in understanding complex multimodal data.

Abstract

We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. Our model integrates dynamic modality weighting and context-adaptive attention, enhancing the extraction of sentiment and aspect-related information by focusing on how textual cues and visual context interact. We tested our approach against several baselines, including traditional text-based models and other multimodal methods. Results from standard Twitter datasets show that AdaptiSent surpasses existing models in precision, recall, and F1 score, and is particularly effective in identifying nuanced inter-modal relationships that are crucial for accurate sentiment and aspect term extraction. This effectiveness comes from the model's ability to adjust its focus dynamically based on the context's relevance, improving the depth and accuracy of sentiment analysis across various multimodal data sets. AdaptiSent sets a new standard for MABSA, significantly outperforming current methods, especially in understanding complex multimodal information.