Sensory Transfer Learning of Cultural Imagery by Integrating Text Sentiment Mining with Cross-regional Pattern Design

Yushu  Cao

doi:10.6180/jase.202603_29(4).0022

Sensory Transfer Learning of Cultural Imagery by Integrating Text Sentiment Mining with Cross-regional Pattern Design

Computer Science and Information Engineering

Yushu CaoThis email address is being protected from spambots. You need JavaScript enabled to view it.

College of Fine Art and Design, Shenyang Normal University, Shenyang 110034 China

Received: July 13, 2025
Accepted: August 12, 2025
Publication Date: August 21, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202603_29(4).0022

This paper proposes a sensory transfer learning method for cultural imagery that integrates text sentiment mining and cross-regional pattern design. Firstly, through text sentiment mining technology, emotional features are extracted from a large amount of text data to construct an emotional semantic space, so as to quantify the emotional expression of cultural images. Secondly, in combination with cross-regional pattern design, deep learning algorithms are utilized to extract and analyze the visual features of patterns from different regions, and a pattern feature library is established. On this basis, through the sensory transfer learning model, the emotional features of the text and the visual features of the patterns are fused to achieve cross-modal mapping of emotion and vision. The experimental results show that this new method can effectively capture the emotional connotation of cultural imagery and transfer it to cross-regional pattern design, providing a new idea and method for the design of cultural and creative products. This research not only enriches the theoretical system of perceptual design of cultural imagery, but also provides technical support for the development of cross-regional cultural and creative industries.

Keywords: sensory transfer learning; cultural imagery; text sentiment mining; cross-regional pattern design; deep learning

[1] G. CooperandK.-S.Tang,(2024) “Pixels and pedagogy: Examining science education imagery by generative artificial intelligence" Journal of Science Education and Technology 33(4): 556–568. DOI: 10.1007/s10956-024-10104-0.
[2] T. S. Terkenli, (2001) “Towards a theory of the landscape: the Aegean landscape as a cultural image" Landscape and urban planning 57(3-4): 197–208. DOI: 10.1016/S0169-2046(01)00204-3.
[3] J. Yu, L. Zhao, S. Yin, and M. Ivanovi´c, (2024) “News recommendation model based on encoder graph neural net work and bat optimization in online social multimedia art education" Computer Science and Information Systems 21(3): 989–1012. DOI: 10.2298/CSIS231225025Y.
[4] Z. A. Rahman, B. A. Talip, and H. Sarirah, (2024) “Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining" International Journal on Perceptive and Cognitive Computing 10(1): 29–39. DOI: 10.31436/ijpcc.v10i1.418.
[5] A. Daisy. “Sentiment Mining: A Data-Driven Approach for Optimizing Digital Marketing Strategies”. In: The Use of Artificial Intelligence in Digital Market ing: Competitive Strategies and Tactics. IGI Global, 2024, 208–225. DOI: 10.4018/978-1-6684-9324-3.ch009.
[6] R. Emanuel, K. Baker, and S. Challons-Lipton, (2016) “Images every American should know: Developing the cultural image literacy assessment-USA" Journal of Visual Literacy 35(4): 215–236. DOI: 10.1080/1051144X.2016.1278089.
[7] H. Yin and Q. Yang, (2024) “Investor sentiment mining based on bi-LSTM model and its impact on stock price bubbles" Studies in Nonlinear Dynamics & Econometrics 28(5): 703–724. DOI: 10.1515/snde-2022-0028.
[8] B. Yang, G. Wu, and H.Yuan, (2023) “Evaluating the interconnection performance of cross-regional road infrastructures based on an integrated micro-pattern approach with fuzzy linguistic operators" Advanced Engineering Informatics 57: 102039. DOI: 10.1016/j.aei.2023.102039.
[9] S. Wang, J. Liu, K. Xu, M. Ji, and F. Yan, (2023) “Cross regional cooperation and counter-market-oriented spatial linkage: a case study of collaborative industrial parks in the Yangtze River Delta Region" International Journal of Environmental Research and Public Health 20(2): 1055. DOI: 10.3390/ijerph20021055.
[10] S. Wang, Y. Zhang, B. Lin, and B. Li. “Interpretable emotion analysis based on knowledge graph and OCCmodel”. In: Proceedings of the 31st ACM Interna tional Conference on Information & Knowledge Manage ment. 2022, 2038–2045. DOI: 10.1145/3511808.355736.
[11] M. A. M. Shaikh, H. Prendinger, and M. Ishizuka. “Alinguistic interpretation of the occ emotion model for affect sensing from text”. In: Affective information processing. Springer, 2009, 45–73. DOI: 10.1007/978-1-84800-306-4_4.
[12] S. F. Zulkifli, M. Gharib, and K. Taveter. “First steps towards building a dictionary of emotional require ments in healthcare and well-being”. In: 2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW). IEEE. 2024, 376–384. DOI: 10.1109/ REW61692.2024.00059.
[13] F. Liu, P. Yang, Y. Shu, F. Yan, G. Zhang, and Y.-J. Liu, (2023) “Emotion dictionary learning with modality attentions for mixed emotion exploration" IEEE Transactions on Affective Computing 15(3): 1289–1302. DOI: 10.1109/TAFFC.2023.3334520.
[14] S. Yin, H. Li, A. A. Laghari, T. R. Gadekallu, G. A. Sampedro, and A. Almadhor, (2024) “An anomaly detection model based on deep auto-encoder and capsule graph convolution via sparrow search algorithm in 6G In ternet of Everything" IEEE Internet of Things Journal 11(18): 29402–29411. DOI: 10.1109/JIOT.2024.3353337.
[15] Y. Jiang and S. Yin, (2023) “Heterogenous-view occluded expression data recognition based on cycle-consistent adversarial network and K-SVD dictionary learning under intelligent cooperative robot environment" Computer Science and Information Systems 20(4): 1869–1883. DOI: 10.2298/CSIS221228034J.
[16] C. Wang, J. Zhang, D. Liu, Y. Cai, and Q. Gu, (2024) “AnAI-poweredproductidentity form design method based on shape grammar and Kansei engineering: Integrating Midjourney and Grey-AHP-QFD" Applied Sciences 14(17): 7444. DOI: 10.3390/app14177444.
[17] J.Wang,J.Zhou,andS.Zhang,(2025)“Applyingshape grammar and BIM for generating the mass modular housing design in Shan Li Chen Jia village, Zhao Yuan, China" Journal of Asian Architecture and Building Engineering 24(1): 178–198. DOI: 10.1080/13467581.2023.2292081.
[18] D.Abu-AridahandH.Ligler,(2024) “Shape grammars and self-built refugee communities: The transformation from tent shelter to customized structure in Irbid Camp" International Journal of Architectural Computing 22(1): 149–172. DOI: 10.1177/14780771241229854.
[19] Y. Shi and A. Economou. “Dougong Revisited: A Parametric Specification of Chinese Bracket Design in Shape Machine”. In: International Conference on Design Computing and Cognition. Springer. 2024, 233 249. DOI: 10.1007/978-3-031-71918-9_15.
[20] S. Nasiri and A. R. Sarvdalir, (2024) “Geometrical Ori gin of Generative Shape Grammars for Islamic Tectonics: Chapireh: The Transforming Element from Cubic Chambers to Spheric Domes" Nexus Network Journal 26(1): 177–195. DOI: 10.1007/s00004-023-00743-y.
[21] S. S. Maram, J. Pfau, J. Villareale, Z. Teng, J. Zhu, and M. S. El-Nasr. “Mining player behavior patterns from domain-based spatial abstraction in games”. In: 2023 IEEE Conference on Games (CoG). IEEE. 2023, 1–8. DOI: 10.1109/CoG57401.2023.10333224.
[22] T. Banerjee, P. Chhabra, M. Kumar, A. Kumar, K. Abhishek, and M. A. Shah, (2025) “Pyramidal attention based T network for brain tumor classification: a com prehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches" Scientific Reports 15(1): 28669. DOI: 10.1038/s41598 025-11574-x.
[23] M.HuandX.Zhao,(2025) “Game as a medium strategies and implications of the international communication of ‘Black Myth Wukong’in the perspective of transcultural communication" International Communication of Chinese Culture: 1–23. DOI: 10.1007/s40636-025-00332-3.
[24] Y. Ren, W. Fan, and J. Wang, (2025) “Intelligent text analysis for effective evaluation of english Language teaching based on deep learning" Scientific Reports 15(1): 28949. DOI: 10.1038/s41598-025-14320-5.