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Multimodal Sentiment Analysis

Multimodal sentiment analysis is the task of performing sentiment analysis with multiple data sources - e.g. a camera feed of someone's face and their recorded speech.

( Image credit: ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection )

Papers

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Showing 176–200 of 202 papers

TitleStatusHype
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences—0
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis—0
TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems—0
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning—0
Toward Robust Multimodal Learning using Multimodal Foundational Models—0
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge—0
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis—0
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis—0
Towards Multimodal Sentiment Analysis Debiasing via Bias Purification—0
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability—0
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey—0
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
Learning Robust Joint Representations for Multimodal Sentiment Analysis—0
Lightweight Models for Multimodal Sequential Data—0
TPFN: Applying Outer Product along Time to Multimodal Sentiment Analysis Fusion on Incomplete Data—0
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis—0
Improving Multimodal fusion via Mutual Dependency Maximisation—0
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis—0
Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis—0
MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation—0
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects—0
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis—0
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