Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Compared to traditional sentiment analysis, which only considers text, multimodal sentiment
analysis needs to consider emotional signals from multimodal sources simultaneously and …

With the increasing popularity of posting multimodal messages online, many recent studies
have been carried out utilizing both textual and visual information for multi-modal sarcasm …

It has been a hot research topic to enable machines to understand human emotions in
multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion …

As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis
(MABSA) has attracted increasing attention in recent years. However, previous approaches …

Compared with unimodal data, multimodal data can provide more features to help the model
analyze the sentiment of data. Previous research works rarely consider token-level feature …

Social media networks have become a significant aspect of people's lives, serving as a
platform for their ideas, opinions and emotions. Consequently, automated sentiment …

With the popularity of social media, detecting sentiment from multimodal posts (eg image-
text pairs) has attracted substantial attention recently. Existing works mainly focus on fusing …

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

Abstract Targeted Multimodal Sentiment Classification (TMSC) aims to identify the sentiment
polarities over each target mentioned in a pair of sentence and image. Existing methods to …