Efficiently capturing consistent and complementary semantic features in a multimodal
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …

Human Multimodal Sentiment Analysis (MSA) is an attractive research that studies sentiment
expressed from multiple heterogeneous modalities. While transformer-based methods have …

Multimodal emotion recognition (MER) integrates multiple modalities to identify the user's
emotional state, which is the core technology of natural and friendly human–computer …

Accurate identification of sentiments in government-related comments is crucial for
policymakers to deeply understand public opinion, adjust policies promptly, and enhance …

With the development of the Internet, the content that people share contains types of text,
images, and videos, and utilizing these multimodal data for sentiment analysis has become …

Multimodal sentiment analysis (MSA) has attracted more and more attention in recent years.
This paper focuses on the representation learning of multimodal data to reach higher …

Multimodal sentiment analysis is an active research area that combines multiple data
modalities, eg, text, image and audio, to analyze human emotions and benefits a variety of …

Multimodal sentiment analysis (MSA) involves interpreting sentiment using various sensory
data modalities. Traditional MSA models often overlook causality between modalities …

Multimodal sentiment analysis (MSA) aims to use a variety of sensors to obtain and process
information to predict the intensity and polarity of human emotions. The main challenges …

Multimodal sentiment analysis has become a popular research topic in recent years.
However, existing methods have two unaddressed limitations:(1) they use limited …