Jul 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 975-989· 0 citations
TL;DR
This work suggests an improved prompt-based multi-modal sentiment analysis (IPMMSA) strategy that incorporates multi-view and diversified knowledge augmentation that yields robust and expressive multimodal embedding’s to boost aspect-based sentiment analysis performance during multimodal integration with multi-modal fashion dataset.
Abstract
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.
An Aspect-guided dual-branch fusion network (ADFN) to enhance sentiment prediction by incorporating external knowledge and integrating coarse and fine information is proposed, which incorporates syntactic dependency information to complement and enrich the textual semantic representations.
Bin Song, Wenjing Liu, Zhipeng Liang et al.· Signal, Image and Video Proc...· 0 citations
Less obvious expressions like sarcasm and implicit sentiment are handled more effectively in this work, improving interpretation in multimodal sentiment analysis of social media data.
Prashant Adakane, Amit Gaikwad· international journal of eng...· 0 citations
With the widespread growth of digital platforms, online interaction has become an essential part of everyday life. Users
frequently express their opinions, feedback, and emotions through reviews and comments on various platforms. Analyzing such
textual data plays a crucial role in understanding user sentiment and supporting effective decision-making. However, sentiment
analysis faces several challenges, including long-range dependencies within text and the presence of unknown words and
symbols. Traditional sentiment analysis approaches mainly rely on sequential models, which process text step by step and often
require higher computational time. In contrast, Transformer-based models offer improved efficiency through parallel
processing. To address these challenges, this paper presents a context-aware hybrid deep learning approach by integrating the
Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks.
RoBERTa is employed to generate rich contextual word embeddings, while BiLSTM captures long-term semantic dependencies
by processing text in both forward and backward directions. The proposed model is trained and evaluated on the Twitter US
Airline Sentiment dataset comprising 14,299 samples across three sentiment classes. Experimental analysis demonstrates that
the hybrid approach achieves an accuracy of 85.14% and an F1-score of 0.8487, highlighting its effectiveness for sentiment
analysis tasks compared to baseline models
Dr. Veguru Gayatri, Dr. Rajani Rajalingam· International Journal for Re...· 0 citations
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.
Shanshan Lin, Yuesheng Wu, Chao Chen et al.· 0 citations
Multimodal sentiment analysis has attracted increasing attention due to the prevalence of text-image content on social media. A central challenge is to design fusion mechanisms that are both expressive and parameter-efficient, especially for small-scale datasets where heavy cross-modal attention can easily overfit. In this paper, we present Consistency-Aware Gated Fusion (CAGF), a lightweight and fusion module tailored to Mamba-based architectures. Our key idea is to exploit Mamba's bidirectional scanning mechanism: forward and backward hidden states from text and image encoders are concatenated to form enhanced representations, and a cosine-based semantic consistency score is computed between modalities. This score is then passed through a fixed sigmoid gate to adaptively weight text and image features, without introducing any additional learnable parameters. CAGF is plug-and-play compatible with dual-stream Mamba encoders and incurs negligible computational overhead compared with attention-based fusion. Experiments on the MVSA-Single dataset show that CAGF achieves state-of-the-art performance (Acc=82.54%, F1=84.82%), outperforming strong multimodal baselines such as CLIP, MISA, DLF, AoM, and SFTTR, while remaining more efficient and interpretable. Extensive ablations and sensitivity analyses further validate that bidirectional scanning, enhanced representations, and consistency-aware gating are all critical to the observed gains.
Jian Hu· Poster Volume 0008 The 2026...· 0 citations
Multimodal aspect-based sentiment analysis (MABSA) predicts the sentiment polarity associated with a specified aspect by jointly exploiting textual and visual information. Existing models may be sensitive to limited prompting examples, cross-modal noise, and unreliable generated context. This paper proposes QMPN, Quality-Aware Memory Prompting Network, that stores sample-specific prompts derived from a small support set, retrieves relevant prompting evidence for each query, and uses the retrieved prompts to guide aspect-aware context generation. A task-oriented quality gate, learned indirectly through the sentiment classification objective, controls the contribution of the generated context to the final prediction. Under the fixed protocol used in this study, QMPN employs 50 labeled support instances for prompt-memory construction and achieves 78.6% accuracy and 74.8% macro-F1 on Twitter-2015, and 72.0% accuracy and 70.5% macro-F1 on Twitter-2017. Relative to the variant without context generation, the complete model improves accuracy/macro-F1 by 3.31/2.83 percentage points on Twitter-2015 and 3.84/3.22 percentage points on Twitter-2017. Ablation and parameter analyses further show the contributions of memory retrieval, adaptive prompt selection, context generation, and quality-aware fusion. Because the evaluation is limited to two historical Twitter benchmarks and a single fixed seed, the reported results should be interpreted within this experimental scope rather than as evidence of universal cross-domain generalization.
Lei Pan, Tong Geng, Yuheng Liu· Information· 0 citations