Sentiment analysis (SA) has gained much traction In the field of artificial intelligence (AI) and
natural language processing (NLP). There is growing demand to automate analysis of user …

Deep learning has emerged as a powerful machine learning technique to employ in
multimodal sentiment analysis tasks. In the recent years, many deep learning models and …

Sentiment analysis is an emerging technology that aims to explore people's attitudes toward
an entity. It can be applied in a variety of different fields and scenarios, such as product …

Multi-modal conversation emotion recognition (MCER) aims to recognize and track the
speaker's emotional state using text, speech, and visual information in the conversation …

Sentiment analysis is a computational technique that analyses the subjective information
conveyed within a given expression. This encompasses appraisals, opinions, attitudes or …

The behaviors of general users for a dialogue system differ greatly from those for a human
interlocutor. We have been collecting a multimodal dialogue corpus between a human …

With the development of multimodal systems and natural language generation techniques,
the resurgence of multimodal datasets has attracted significant research interests, which …

Social media comments are no longer in a single textual modality, but heterogeneous data
in multiple modalities, such as vision, sound, and text, which is why multimodal sentiment …

Recently, contrastive learning has begun to gain popularity in multimodal sentiment analysis
(MSA). However, most of existing MSA methods based on contrastive learning lacks more …

Multi-modal fusion technology gradually become a fundamental task in many fields, such as
autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction …