arXiv AI August 11, 2026 By Jiaqi Qiao, Xinran Li, Yifan Lyu, Xiujuan Xu, Liu Yu

Expert-Guided Multimodal Fusion for Unified Emotion and Sentiment Analysis

arXiv:2601. 07565v2 Announce Type: replace-cross Abstract: Multimodal emotion understanding requires the integration of heterogeneous data sources, including text, audio, and visual modalities, while simultaneously addressing discrete emotion recognition and continuous sentiment analysis.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 16

MAF: Multimodal Adaptive Few-shot Prompting for Sentiment Analysis with MLLMs

arXiv:2606. 15694v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in understanding complex multimodal content.

By Hangling Xie

llms nlp multimodal benchmarks

arXiv AI

Aug 6

C$^2$MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning

arXiv:2608. 04013v1 Announce Type: cross Abstract: Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs.

By Yuntao Shou, Tao Meng, Wei Ai, Keqin Li

multimodal benchmarks

arXiv Machine Learning

1d ago

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

arXiv:2608. 16201v1 Announce Type: new Abstract: Multimodal sentiment analysis (MSA) aims to predict sentiment polarity and intensity from heterogeneous inputs such as text, audio, and vision.

By Shanshan Lin, Yuesheng Wu, Chao Chen, Yizhe Yang, Zhihao Chen, Zexian Yang, Xiangwen Liao

llms nlp multimodal benchmarks safety

arXiv AI

Jul 9

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

arXiv:2607. 06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words.

By Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu

llms nlp efficiency multimodal benchmarks

arXiv Machine Learning

Jun 3

Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals

arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.

By Jiyuan Liu, Liangwei Nathan Zheng, Wei Emma Zhang, Xinpei Wang, Weitong Chen

multimodal benchmarks

arXiv AI

Jul 15

Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?

arXiv:2607. 12787v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc.

By Kaiwen Zheng, Junchen Fu, Wenhao Deng, Hu Han, Joemon M. Jose, Xuri Ge

llms robotics efficiency multimodal benchmarks safety

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MAF: Multimodal Adaptive Few-shot Prompting for Sentiment Analysis with MLLMs

C$^2$MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning

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