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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 111–120 of 202 papers

TitleStatusHype
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis—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
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis—0
MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation—0
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis—0
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality—0
Modality Influence in Multimodal Machine Learning—0
Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis—0
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