Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions
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 …
natural language processing (NLP). There is growing demand to automate analysis of user …
State of the art: a review of sentiment analysis based on sequential transfer learning
Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
Multimodal sentiment analysis based on fusion methods: A survey
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 …
an entity. It can be applied in a variety of different fields and scenarios, such as product …
Decoupled multimodal distilling for emotion recognition
Human multimodal emotion recognition (MER) aims to perceive human emotions via
language, visual and acoustic modalities. Despite the impressive performance of previous …
language, visual and acoustic modalities. Despite the impressive performance of previous …
Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis
Abstract Representation Learning is a significant and challenging task in multimodal
learning. Effective modality representations should contain two parts of characteristics: the …
learning. Effective modality representations should contain two parts of characteristics: the …
Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis
In multimodal sentiment analysis (MSA), the performance of a model highly depends on the
quality of synthesized embeddings. These embeddings are generated from the upstream …
quality of synthesized embeddings. These embeddings are generated from the upstream …
Disentangled representation learning for multimodal emotion recognition
Multimodal emotion recognition aims to identify human emotions from text, audio, and visual
modalities. Previous methods either explore correlations between different modalities or …
modalities. Previous methods either explore correlations between different modalities or …
Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis
Multimodal sentiment analysis aims to extract and integrate semantic information collected
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …
Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research
Sentiment analysis as a field has come a long way since it was first introduced as a task
nearly 20 years ago. It has widespread commercial applications in various domains like …
nearly 20 years ago. It has widespread commercial applications in various domains like …
Dynamic interactive multiview memory network for emotion recognition in conversation
When available, multimodal data is key for enhanced emotion recognition in conversation.
Text, audio, and video in dialogues can facilitate and complement each other in analyzing …
Text, audio, and video in dialogues can facilitate and complement each other in analyzing …