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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Enhanced Quantum-Inspired Deep Learning with Multi-Head Attention and Contrastive Learning for Text-Based Dialogue Sentiment Classification

by
Fumin Zou
1,2,
Lei Zou
1,2,
Feng Guo
1,2,*,
Xunhuang Wang
1,2,
Jianqing Weng
1,2,
Tao Fang
1,2,
Haocai Jiang
1,2 and
Xueming Wu
1,2
1
Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China
2
Renewable Energy Technology Research Institute of Fujian University of Technology, Fujian University of Technology, Ningde 352101, China
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2026, 10(5), 161; https://doi.org/10.3390/bdcc10050161
Submission received: 13 March 2026 / Revised: 7 May 2026 / Accepted: 15 May 2026 / Published: 18 May 2026
(This article belongs to the Special Issue Advances in Natural Language Processing and Text Mining: 2nd Edition)
Versions Notes

Abstract

This study introduces the Quantum-inspired Pretrained Feature Embedding (ImprovedQPFE) model, a framework for dialogue sentiment classification. ImprovedQPFE integrates phase-pretrained complex embeddings, a bidirectional complex-valued GRU, a quantum-inspired attention mechanism, and supervised contrastive learning within a Transformer-based architecture, aiming to enhance feature discriminability under class imbalance. We evaluate ImprovedQPFE on the RECCON-DD and RECCON-IEM benchmarks under a unified and reproducible protocol, including standardized preprocessing and fixed data splits. To ensure reproducibility, all experiments were conducted using a fixed random seed of 42. The reported results are based on this single fixed-seed setting rather than averages over multiple repeated runs. The empirical results show that ImprovedQPFE achieves competitive performance and outperforms the compared baselines under the adopted experimental protocol. On the RECCON-DD dataset, ImprovedQPFE improves Macro-F1 from 80.08% to 83.75% compared with a strong non-quantum Transformer-based baseline equipped with contrastive learning. It also improves Pos-F1 while maintaining high performance for negative classes. On RECCON-IEM, ImprovedQPFE attains a leading Macro-F1 of 95.39% among the compared methods. These findings, together with an ablation analysis, support the effectiveness of the proposed quantum-inspired representation paradigm and its architectural components. However, further statistical validation with multiple repeated runs, standard deviations, confidence intervals, and significance testing remains an important direction for future work.
Keywords: quantum-inspired computing; deep learning; multi-head attention; contrastive learning; emotion recognition quantum-inspired computing; deep learning; multi-head attention; contrastive learning; emotion recognition

Share and Cite

MDPI and ACS Style

Zou, F.; Zou, L.; Guo, F.; Wang, X.; Weng, J.; Fang, T.; Jiang, H.; Wu, X. Enhanced Quantum-Inspired Deep Learning with Multi-Head Attention and Contrastive Learning for Text-Based Dialogue Sentiment Classification. Big Data Cogn. Comput. 2026, 10, 161. https://doi.org/10.3390/bdcc10050161

AMA Style

Zou F, Zou L, Guo F, Wang X, Weng J, Fang T, Jiang H, Wu X. Enhanced Quantum-Inspired Deep Learning with Multi-Head Attention and Contrastive Learning for Text-Based Dialogue Sentiment Classification. Big Data and Cognitive Computing. 2026; 10(5):161. https://doi.org/10.3390/bdcc10050161

Chicago/Turabian Style

Zou, Fumin, Lei Zou, Feng Guo, Xunhuang Wang, Jianqing Weng, Tao Fang, Haocai Jiang, and Xueming Wu. 2026. "Enhanced Quantum-Inspired Deep Learning with Multi-Head Attention and Contrastive Learning for Text-Based Dialogue Sentiment Classification" Big Data and Cognitive Computing 10, no. 5: 161. https://doi.org/10.3390/bdcc10050161

APA Style

Zou, F., Zou, L., Guo, F., Wang, X., Weng, J., Fang, T., Jiang, H., & Wu, X. (2026). Enhanced Quantum-Inspired Deep Learning with Multi-Head Attention and Contrastive Learning for Text-Based Dialogue Sentiment Classification. Big Data and Cognitive Computing, 10(5), 161. https://doi.org/10.3390/bdcc10050161

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