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Open access Aug 2026

Image–Text Multimodal Sentiment Analysis with Large Model-Generated Descriptive Semantics and Difference-Aware Gated Fusion

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.

Hengyuan Zhang, Aizihaierjiang Yusufu, Jiang Liu et al. · 0 citations
#small language model Preprint Aug 2026

Multi-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis

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
Open access Aug 2026

Bridging Visual and Textual Cues: Cross-Attention Fusion for Multi-Modal Sentiment Analysis

A hybrid Deep Auto-Encoding with Convolutional Neural Networks (DAE+CNN) is presented, a multi-modal technique based on cross-attention based multi-modal fusion model for text and visuals that outperforms single-modal sentiment analysis.

M. Yuvaraja, Dr. C. Kumuthini · 0 citations
Conference Sep 2026

A semantic modeling-based NLP approach to sentiment analysis of film and television reviews

Data experiments and kernel density distribution analysis confirm that this architecture maintains low computational inference latency while ensuring effective feature extraction, providing a feasible engineering algorithm reference architecture for sentiment analysis of unstructured user review texts.

Yu-Si Qi · 0 citations
Conference 2026

Hierarchical Global-Local Interaction and Refinement for Multimodal Sentiment Analysis

A Modality Dropout strategy is first introduced at the input stage to alleviate over-reliance on a sin-gle modality and improve robustness and the proposed Hierarchical Global-Local Interaction and Refinement framework for Multimodal Sentiment Analysis (HGLIR) is proposed.

yuanyuan zhou · 0 citations
Aug 2026

Aspect-guided dual-branch fusion network for multimodal aspect-based sentiment analysis

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, Zhi Liang et al. · 0 citations

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