A comprehensive survey on deep learning-based approaches for multimodal sentiment analysis
A Ghorbanali, MK Sohrabi - Artificial Intelligence Review, 2023 - Springer
Sentiment analysis is an important natural language processing issue that has many
applications in various fields. The increasing popularity of social networks and growth and …
applications in various fields. The increasing popularity of social networks and growth and …
Sentiment analysis: Comprehensive reviews, recent advances, and open challenges
Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with
computers. Previous studies have achieved significant breakthroughs and extensive …
computers. Previous studies have achieved significant breakthroughs and extensive …
Sentiment-aware multimodal pre-training for multimodal sentiment analysis
Pre-trained models, together with fine-tuning on downstream labeled datasets, have
demonstrated great success in various tasks, including multimodal sentiment analysis …
demonstrated great success in various tasks, including multimodal sentiment analysis …
A fine-grained modal label-based multi-stage network for multimodal sentiment analysis
Sentiment analysis is a challenging but valuable research topic in affective computing. It can
improve the quality of various real-world applications, including financial market prediction …
improve the quality of various real-world applications, including financial market prediction …
TEDT: transformer-based encoding–decoding translation network for multimodal sentiment analysis
F Wang, S Tian, L Yu, J Liu, J Wang, K Li… - Cognitive Computation, 2023 - Springer
Multimodal sentiment analysis is a popular and challenging research topic in natural
language processing, but the impact of individual modal data in videos on sentiment …
language processing, but the impact of individual modal data in videos on sentiment …
Inter-intra modal representation augmentation with trimodal collaborative disentanglement network for multimodal sentiment analysis
Recently, Multimodal Sentiment Analysis (MSA) is a challenging research area given its
complex nature, and humans express emotional cues across various modalities such as …
complex nature, and humans express emotional cues across various modalities such as …
Shared and private information learning in multimodal sentiment analysis with deep modal alignment and self-supervised multi-task learning
Designing an effective representation learning method for multimodal sentiment analysis
tasks is a crucial research direction. The challenge lies in learning both shared and private …
tasks is a crucial research direction. The challenge lies in learning both shared and private …
Rba-gcn: Relational bilevel aggregation graph convolutional network for emotion recognition
L Yuan, G Huang, F Li, X Yuan… - … /ACM Transactions on …, 2023 - ieeexplore.ieee.org
Emotion recognition in conversation (ERC) has received increasing attention from
researchers due to its wide range of applications. As conversation has a natural graph …
researchers due to its wide range of applications. As conversation has a natural graph …
A cross modal hierarchical fusion multimodal sentiment analysis method based on multi-task learning
L Wang, J Peng, C Zheng, T Zhao - Information Processing & …, 2024 - Elsevier
Humans often express affections and intentions through multiple forms when
communicating, involving text, audio, and vision modalities. Using a single modality to …
communicating, involving text, audio, and vision modalities. Using a single modality to …
Scanning, attention, and reasoning multimodal content for sentiment analysis
Y Liu, Z Li, K Zhou, L Zhang, L Li, P Tian… - Knowledge-Based …, 2023 - Elsevier
The rise of social networks has provided people with platforms to display their lives and
emotions, often in multimodal forms such as images and descriptive texts. Capturing the …
emotions, often in multimodal forms such as images and descriptive texts. Capturing the …