April 25, 2024IEEE Transactions on Industrial Informatics

Multichannel Cross-Modal Fusion Network for Multimodal Sentiment Analysis Considering Language Information Enhancement

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A multichannel cross-modal fusion network enhances multimodal sentiment analysis by suppressing auxiliary redundancy and strengthening the primary language modality across video tasks.
Benchmark evaluation on CMU-MOSI, CMU-MOSEI, and Twitter2019 datasets utilizes a three-channel gating framework to merge representations from distinct visual, acoustic, and text streams.
Targeted enhancement of dominant text channels combined with selective feature filtering provides a robust architecture for fine-grained sentiment classification in human-computer systems.

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Implication

Benchmark evaluation shows improved sentiment classification across video datasets, highlighting the benefit of reducing auxiliary redundancy while enhancing the primary language modality.

Cite This Study

Hu et al. (2024) studied this question.

synapsesocial.com/papers/68e6d971b6db6435876556aa https://doi.org/10.1109/tii.2024.3388670

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Implication

Benchmark evaluation shows improved sentiment classification across video datasets, highlighting the benefit of reducing auxiliary redundancy while enhancing the primary language modality.

Multichannel Cross-Modal Fusion Network for Multimodal Sentiment Analysis Considering Language Information Enhancement

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Discussion

Implication

Cite This Study

Multichannel Cross-Modal Fusion Network for Multimodal Sentiment Analysis Considering Language Information Enhancement

Key Points

Discussion

Implication

Cite This Study

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