Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.09.117
Abstract: Abstract The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with… read more here.
Keywords: multiple kernel; analysis; multimodal sentiment; kernel learning ... See more keywords
Adaptive multimodal transformer based on exchanging for multimodal sentiment analysis
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DOI: 10.1038/s41598-025-11848-4
Abstract: Multimodal sentiment analysis significantly improves sentiment classification performance by integrating cross-modal emotional cues. However, existing methods still face challenges in key issues such as modal distribution differences, cross-modal interaction efficiency, and contextual correlation modeling. To… read more here.
Keywords: modality; adaptive multimodal; cross modal; multimodal sentiment ... See more keywords
MEDT: Using Multimodal Encoding-Decoding Network as in Transformer for Multimodal Sentiment Analysis
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DOI: 10.1109/access.2022.3157712
Abstract: Multimodal sentiment analysis is a challenging task in the field of natural language processing (NLP). It uses multimodal signals (natural language, facial gestures, and acoustic behavior) in videos to generate emotional understanding. However, the importance… read more here.
Keywords: natural language; network transformer; multimodal sentiment; network ... See more keywords
AdaMoW: Multimodal Sentiment Analysis Based on Adaptive Modality-Specific Weight Fusion Network
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DOI: 10.1109/access.2023.3276932
Abstract: Multimodal sentiment analysis (MSA) is a crucial task in the field of natural language processing (NLP), with a wide range of applications. This paper proposes an adaptive modality-specific weight fusion network (AdaMoW) to address issues… read more here.
Keywords: network; fusion; sentiment; modality ... See more keywords
Federated Learning for Multimodal Sentiment Analysis: Advancing Global Models With an Enhanced LinkNet Architecture
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DOI: 10.1109/access.2024.3503290
Abstract: Analyzing sentiments using single-modal approaches, such as text or image analysis alone, frequently encounters significant limitations. These drawbacks include inadequate feature representation, an inability to capture the full complexity of emotional expressions, and challenges in… read more here.
Keywords: analysis; model; multimodal sentiment; approach ... See more keywords
Self-supervised Guided Modality Disentangled Representation Learning for Multimodal Sentiment Analysis and Schizophrenia Assessment.
Sign Up to like & getrecommendations! Published in 2025 at "IEEE journal of biomedical and health informatics"
DOI: 10.1109/jbhi.2025.3604933
Abstract: As the impact of chronic mental disorders increases, multimodal sentiment analysis (MSA) has emerged to improve diagnosis and treatment. In this paper, our approach leverages disentangled representation learning to address modality heterogeneity with self-supervised learning… read more here.
Keywords: self supervised; representation; representation learning; multimodal sentiment ... See more keywords
Multilevel Representation Disentanglement Framework for Multimodal Sentiment Analysis
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DOI: 10.1109/lsp.2025.3562827
Abstract: Multimodal Sentiment Analysis (MSA) has gained wide attention in many fields in recent years. However, the problem of heterogeneity and redundant information among different signals seriously affects the extraction and fusion of sentiment features. To… read more here.
Keywords: disentanglement framework; framework; multimodal sentiment; sentiment ... See more keywords
Learning Three-Modal Joint Representations With Multimodal Transformer for Multimodal Sentiment Analysis
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Consumer Electronics"
DOI: 10.1109/tce.2025.3593333
Abstract: Understanding user sentiment from multiple modalities is essential for applications such as personalized recommendations and affective computing. We propose TMJR, a Three-modal Joint Representation model for multimodal sentiment analysis, which introduces a structured three-round cross-modal… read more here.
Keywords: three modal; modal joint; multimodal sentiment; sentiment ... See more keywords
Video-Based Cross-Modal Auxiliary Network for Multimodal Sentiment Analysis
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DOI: 10.1109/tcsvt.2022.3197420
Abstract: Multimodal sentiment analysis has a wide range of applications due to its information complementarity in multimodal interactions. Previous works focus more on investigating efficient joint representations, but they rarely consider the insufficient unimodal features extraction… read more here.
Keywords: sentiment analysis; cross modal; multimodal sentiment; sentiment ... See more keywords
Multimodal Sentiment Analysis of Online Product Marketing Information Based on Artificial Intelligence Neural Networks and Text Mining
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DOI: 10.1109/tem.2025.3589199
Abstract: With the rise of multimodal content (such as text and images) in online product marketing, sentiment analysis techniques face increasing demands for accuracy and versatility. However, existing approaches often struggle with modality coordination, deep emotional… read more here.
Keywords: online product; marketing; multimodal sentiment; sentiment ... See more keywords
Meta Noise Adaption Framework for Multimodal Sentiment Analysis With Feature Noise
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2024.3362600
Abstract: Improving the robustness of models against feature noise has emerged as one of the most crucial research topics in the field of multimodal sentiment analysis. Recent studies assume that the training instances are free of… read more here.
Keywords: adaption; feature noise; multimodal sentiment; sentiment analysis ... See more keywords