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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 101–150 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
Multimodal Sentiment Analysis Based on BERT and ResNet—0
Multimodal Sentiment Analysis Based on Causal Reasoning—0
Multimodal Sentiment Analysis based on Video and Audio Inputs—0
Multimodal Sentiment Analysis on CMU-MOSEI Dataset using Transformer-based Models—0
Multimodal Sentiment Analysis: Perceived vs Induced Sentiments—0
Analyzing Modality Robustness in Multimodal Sentiment Analysis—0
A Multimodal Sentiment Dataset for Video Recommendation—0
Multimodal Sentiment Analysis with Common-sense Modulation—0
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach—0
Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis—0
Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs—0
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos—0
Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis—0
Curriculum Learning Meets Weakly Supervised Modality Correlation Learning—0
Neural Dependency Coding inspired Multimodal Fusion—0
Cross-Modality Gated Attention Fusion for Multimodal Sentiment Analysis—0
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss—0
Polarity and Intensity: the Two Aspects of Sentiment Analysis—0
PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis—0
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment—0
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture—0
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis—0
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