TL;DR
This work proposes a novel framework that employs the pre-trained vision-language model BLIP (Bootstrapping Language-Image Pre-training) to generate descriptive image captions and introduces an aspect-guided soft prompt mechanism that enables dynamic interaction between aspect terms and multimodal features, thereby mitigating the effects of structural irregularities.
Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to simultaneously extract aspects and predict their sentiment polarities from paired textual and visual content. Existing approaches typically formulate MABSA as either a classification or index generation task, which may limit the accuracy of aspect boundary localization, an essential component of end-to-end aspect-sentiment prediction. To address this issue, we propose TicCondDiffusion, a Tweet-Image-Caption conditioned diffusion model that reformulates MABSA as a denoising process over aspect boundary coordinates for aspect boundary localization and sentiment prediction, where the caption is an image-derived textual description used as auxiliary visual-semantic guidance. TicCondDiffusion consists of two main processes: a noising process and a conditioned denoising process. In the noising process, Gaussian noise is gradually added to ground-truth boundary coordinates over multiple timesteps to generate noisy boundary coordinates. In the denoising process, the denoising module, conditioned on Tweet-Image-Caption representations and timestep embeddings, recovers noisy boundary coordinates for boundary localization while simultaneously predicting the sentiment polarities of the corresponding aspects, with subsequent denoising steps providing further refinement. To effectively exploit multimodal information, TicCondDiffusion incorporates three specialized fusion modules: a Tweet-Image Fusion Module, a Tweet-Caption Fusion Module, and a Tweet Feature Fusion Module. Experimental results on Twitter-2015 and Twitter-2017 demonstrate that TicCondDiffusion achieves competitive performance compared with recent baseline approaches under the end-to-end setting. Experiments on the Political-Twitter dataset further show its adaptability to domains with different topical distributions. Additional analyses further validate the effectiveness and flexibility of the proposed framework. To the best of our knowledge, TicCondDiffusion is the first diffusion-based framework specifically designed for MABSA, offering a new direction for this task (The code is available on https://github.com/MEIMEIMEIMEIMEMEDA/TicCondDiffusion.).
MGSI first encodes audio and visual streams at short-, medium-, and long-range temporal scales, preserving both local variations and global affective trends, and applies polarity- and intensity-aware enhancement to better handle ambiguous and near-neutral samples.
Image–text multimodal sentiment analysis aims to integrate textual and visual information to comprehensively understand sentiment expressions in complex scenarios. However, existing methods focus on cross-modal feature interaction and fusion, and still have difficulty capturing effective sentiment cues in scenarios involving insufficient textual semantics, implicit visual affective cues, and inconsistent sentiment expressions between text and image. To address these issues, this paper proposes an image–text multimodal sentiment analysis method with large model-generated descriptive semantics and difference-aware gated fusion. Specifically, a large model generates semantic descriptions for image–text pairs, from which an enhanced semantic view is constructed to supplement implicit or insufficiently expressed sentiment cues in the original modalities. An original-enhanced dual-branch structure models the original image–text evidence and enhanced semantic evidence separately. To improve semantic consistency between the two branches, a cross-branch semantic alignment mechanism is introduced to reduce semantic shifts caused by enhanced information. In the fusion stage, difference-aware gated fusion and residual compensation are employed to adaptively balance branch contributions while preserving discriminative branch differences. Experimental results on the MVSA-Single and MVSA-Multiple datasets show that the proposed method improves performance in image–text multimodal sentiment classification, thereby validating the effectiveness of combining semantic enhancement with difference-aware modeling.
To enhance pertinent decision-making in a variety of applications, prompt-based sentiment analysis attempts to leverage cross-modal opinion signals to analyze users’ attitude direction regarding the specific attribute. Even though many techniques have been created, they are unable to use multiple knowledge types at once and are unable to successfully eliminate unwanted signals from various viewpoints, which can impair multimodal representations' discriminative power and keep models from performing better. To fill up the research gaps, this work suggests an improved prompt-based multi-modal sentiment analysis (IPMMSA) strategy that incorporates multi-view and diversified knowledge augmentation. In particular, it implements fine-grained image-aspect interactions by transforming the image into an underlying sequence of embedding’s which makes filtering easier from a visual semantic standpoint. Attribute-guided vision-language interactions are then used to pull out important extract affective signals and suppress irrelevant content within a multimodal semantic framework, while the network structure is formulated to effectively exploit context-informed semantic fusion, syntactic relations, and sentiment-aware domain knowledge. Ultimately, the model yields robust and expressive multimodal embedding’s to boost aspect-based sentiment analysis performance during multimodal integration with multi-modal fashion dataset. Finally, to show the superiority along with efficacy of our suggested approach extensive experiments were conducted on two widely used multi-modal fashion datasets.
MagXCL, a unified framework designed to improve multimodal integration through more effective interaction between verbal and non-verbal modalities, is proposed, demonstrating the effectiveness of combining AMag with CrossCL to produce more accurate and robust multimodal sentiment predictions.