Multimodal Sentiment Analysis
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Benchmarks
Most implemented
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Multimodal Transformer for Unaligned Multimodal Language Sequences
Multimodal Speech Emotion Recognition Using Audio and Text
TVLT: Textless Vision-Language Transformer
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
Papers
Contrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination
Incomplete multimodal sentiment analysis has garnered significant attention in recent years. Existing approaches typically assume that data is missing at random or are designed specifically for certain missing patterns, …
Multimodal Sentiment AnalysisContrastive LearningRobust Incomplete Multimodal Sentiment Analysis via Iterative Proxy Correction
Multimodal sentiment analysis aims to infer affective states by integrating language, visual, and acoustic cues. However, real-world multimodal inputs are often incomplete or corrupted, which can weaken cross-modal compl…
Multimodal Sentiment AnalysisMultimodal ReasoningMulti-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) aims to predict sentiment polarity and intensity from heterogeneous inputs such as text, audio, and vision. While large language models (LLMs) offer strong semantic priors for MSA, eff…
Multimodal Sentiment AnalysisRethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations
Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete. Existing methods for incomplete-observation MSA mainly follow two…
Multimodal Sentiment AnalysisRepresentation LearningSemantic-Aligned Structural Abstraction for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) aims to interpret complex human emotions by integrating natural language with non-verbal modalities. Non-verbal modalities share a structural isomorphism with natural language, as both…
Multimodal Sentiment AnalysisMRUF: Multi-granularity Routing with Uncertainty-Aware Fusion for Robust Multimodal Sentiment Analysis
Multimodal sentiment analysis relies on language, visual, and acoustic cues, but utterance-level modality quality may vary due to occlusion, background noise, motion blur, or imperfect transcripts, causing conventional f…
Multimodal Sentiment Analysis