Multimodal fusion focuses on integrating information from multiple modalities with the goal of
more accurate prediction, which has achieved remarkable progress in a wide range of …

The new era of technology has brought us to the point where it is convenient for people to
share their opinions over an abundance of platforms. These platforms have a provision for …

The inherent challenge of multimodal fusion is to precisely capture the cross-modal
correlation and flexibly conduct cross-modal interaction. To fully release the value of each …

Huge amounts of multimodal content and comments in a mixture form of text, image, and
emoji are continuously shared by users on various social networks. Most of the comments of …

Previous Sentiment Analysis (SA) studies have demonstrated that exploring sentiment cues
from multiple synchronized modalities can effectively improve the SA results. Unfortunately …

Multimodal data can convey user emotions and feelings more effectively and interactively
than unimodal content. Thus, multimodal sentiment analysis (MSA) research has recently …

To improve the effect of multimodal negative sentiment recognition of online public opinion
on public health emergencies, we constructed a novel multimodal fine-grained negative …

Social media and web have become popular platforms of information sharing, knowledge
gathering, expressing sentiments, perceiving choices regarding products and services …

Recently, uncertain missing modalities in multimodal sentiment analysis (MSA) brings a new
challenge for sentiment analysis. However, existing research cannot accurately complete …

A growing number of individuals are expressing their opinions and engaging in interactive
communication with others through various modalities, including natural language (text) …