AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
This work addresses sentiment analysis for social media users by enhancing accuracy in multimodal data, though it appears incremental as it builds on existing multimodal methods with novel attention mechanisms.
The paper tackled the problem of Multimodal Aspect-Based Sentiment Analysis by introducing AdaptiSent, a framework that uses adaptive cross-modal attention to improve sentiment classification and aspect term extraction from text and images, resulting in surpassing existing models in precision, recall, and F1 score on Twitter datasets.
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.