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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 …
TETFN: A text enhanced transformer fusion network for multimodal sentiment analysis
Multimodal sentiment analysis (MSA), which aims to recognize sentiment expressed by
speakers in videos utilizing textual, visual and acoustic cues, has attracted extensive …
speakers in videos utilizing textual, visual and acoustic cues, has attracted extensive …
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 …
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 …
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 …
Revisiting disentanglement and fusion on modality and context in conversational multimodal emotion recognition
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 …
multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion …