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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 76–100 of 202 papers

TitleStatusHype
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis—0
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content—0
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis—0
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis—0
Modality Influence in Multimodal Machine Learning—0
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality—0
Emoji Driven Crypto Assets Market Reactions—0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification—0
Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis—0
DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam—0
MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation—0
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability—0
DNN Multimodal Fusion Techniques for Predicting Video Sentiment—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis—0
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis—0
Lightweight Models for Multimodal Sequential Data—0
An AutoML-based Approach to Multimodal Image Sentiment Analysis—0
Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis—0
Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis—0
Learning Robust Joint Representations for Multimodal Sentiment Analysis—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
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