A study of sentiment classification methods with dual model decision fusion

Hao Liu; Changhui Liu; Ying Ou

doi:10.1117/12.2640838

13 October 2022 A study of sentiment classification methods with dual model decision fusion

Hao Liu, Changhui Liu, Ying Ou

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228727 (2022) https://doi.org/10.1117/12.2640838
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Sentiment recognition is an important part of sentiment computing. To address the problem that traditional Bert models are studied only in textual aspects and a single model has significant uncertainty, an emotion classification recognition method using CM-BERT and Bi-LSTM-DenseNet dual-model decision fusion is proposed. Using both feature-level fusion and decision-level fusion strategies, the superiority of the dual model in multimodal sentiment recognition is highlighted, and the conclusion that the dual model outperforms the single model in multimodal recognition is verified. The model implemented in this paper achieves 64.5% accuracy on a homemade multimodal dataset, a 1% improvement compared to the CM-BERT model, and a 7% improvement compared to the Bi-LSTM-DenseNet model.

Citation Download Citation

Hao Liu, Changhui Liu, and Ying Ou "A study of sentiment classification methods with dual model decision fusion", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228727 (13 October 2022); https://doi.org/10.1117/12.2640838

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KEYWORDS

Data modeling

Convolution

Performance modeling

Network architectures

Matrices

Neural networks

Speaker recognition

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