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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 101–125 of 202 papers

TitleStatusHype
Gated Mechanism for Attention Based Multimodal Sentiment Analysis—0
Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages—0
Exploring Multimodal Sentiment Analysis via CBAM Attention and Double-layer BiLSTM Architecture—0
Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions—0
Exploiting Diverse Feature for Multimodal Sentiment Analysis—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
Multi-label Emotion Analysis in Conversation via Multimodal Knowledge Distillation—0
Explainable Multimodal Sentiment Analysis on Bengali Memes—0
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content—0
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Emoji Driven Crypto Assets Market Reactions—0
Multimodal fusion via cortical network inspired losses—0
Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification—0
DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam—0
DNN Multimodal Fusion Techniques for Predicting Video Sentiment—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space—0
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition—0
DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis—0
Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis—0
Multimodal Sentiment Analysis—0
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines—0
Multimodal Sentiment Analysis: A Survey—0
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