Leather Apparel Design and Sustainable Fashion Communication Based on Film and Television Media
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Abstract
Existing research on sustainable fashion communication lacks effective analysis of the visual-symbol transformation mechanism in film and television media. This paper proposes a hybrid method integrating visual content analysis and dynamic theme modeling. First, a visual-feature encoding framework for leather clothing in film and television is constructed. Then, the BERTopic model is applied to perform semantic clustering of related film and television texts and public comments. The model uses BERT to generate contextual embeddings, UMAP for dimensionality reduction, HDBSCAN for density clustering, and c-TF-IDF for latent theme extraction. Finally, cross-modal association analysis quantifies the mapping relationship between visual elements and semantic themes. From an engineering communication perspective, the framework offers a signal-encoding and cross-modal interpretation method that can also inform visual evaluation of smart materials, wearable surfaces, and electromagnetic-compatible product narratives without changing the fashion-communication focus of the study. Experimental results show that glossy leather and the theme of ethical consumption exhibit a Cramer V coefficient of 0.41, while cross-type comparisons show the strongest association between distressed finishes and durable classics in drama films, reaching 0.51. This study provides a quantifiable analytical paradigm for the dissemination of sustainable consumer culture driven by visual media.
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References
S. G. Alzahrani and S. A. Saroukh, “The Semiorc Dimension of Men’s Fashion in Modern Eras,” Internaronal Journal, vol. 12, no. 5, pp. 133-148, 2024, doi: 10.11648/j.ijla.20241205.13.
D. Burato, “Audiovisual Narrarve for Fashion Heritage: Communicarng and Valorizing Archives through Fashion Film,” ZoneModa Journal, vol. 14, no. 2, pp. 77-93, 2024, doi: 10.6092/issn.2611-0563/20692.
I. D’Adamo, M. Gagliarducci, M. Iannilli, and V. Mangani, “Fashion Wears Sustainable Leather: A Social and Strategic Analysis Toward Sustainable Producron and Consumpron Goals,” Sustainability, vol. 16, no. 22, pp. 9971, 2024, doi: 10.3390/su16229971.
P. Díaz Soloaga, G. Muñoz Domínguez, and A. G. Woodside, “Fashion and film stories of (mis) understanding: Introducron to a special issue on cinema and fashion,” Journal of Global Fashion Markerng, vol. 14, no. 4, pp. 369-373, 2023, doi: 10.1080/20932685.2023.2206411.
N. Y. Kil and J. Chun, “Semiorc Analysis of Adverrsing Video Related to the Sustainability of Fast Fashion Brands,” Journal of the Korean Society of Clothing and Texrles, vol. 47, no. 6, pp. 1057-1079, 2023, doi: 10.5850/JKSCT.2023.47.6.1057.
E. Ertürk, “Consumer society and sustainability: A semiorc analysis of campaigns on ecological fashion thought,” Ordu Üniversitesi Sosyal Bilimler Ensrtüsü Sosyal Bilimler Ara¸szrmaları Dergisi, vol. 15, no. 1, pp. 227-255, 2025, doi: 10.48146/odusobiad.1424243.
F. Yin, “Analysis of the Role of Costume Design in Shaping the Characters of Film and Television,” Fronrers in Art Research, vol. 5, no. 6, pp. 58-63, 2023, doi: 10.25236/FAR.2023.050611.
J. Clemens-Smucker, “The Clothes Make the Woman: How Fashion Informs the Comedic Idenrty of Schi|’s Creek’s Moira Rose,” Journal of Popular Film and Television, vol. 51, no. 4, pp. 174-183, 2023, doi: 10.1080/01956051.2023.2275011.
A. Chandrasekar, S. E. Clark, S. Marrn, S. Vanderslo|, E. C. Flores, D. Aceituno, et al., “Making the most of big qualitarve datasets: a living systemarc review of analysis methods,” Fronrers in big Data, vol. 7, Art. no. 1455399, 2024, doi: 10.3389/fdata.2024.1455399.
A. Malandrino, “Comparing qualitarve and quanrtarve text analysis methods in combinaron with document-based social network analysis to understand policy networks,” Quality & Quanrty, vol. 58, no. 3, pp. 2543-2570, 2024, doi: 10.1007/s11135-023-01753-1.
R. Murakami and B. Chakraborty, “Invesrgarng the efficient use of word embedding with neural-topic models for interpretable topics from short texts,” Sensors, vol. 22, no. 3, pp. 852, 2022, doi: 10.3390/s22030852.
F. Ravenda, S. A. Bahrainian, A. Raballo, A. Mira, and F. Crestani, “A self-supervised seed-driven approach to topic modeling and clustering,” Journal of Intelligent Informaron Systems, vol. 63, no. 1, pp. 333-353, 2025, doi: 10.1007/s10844-024-00891-8.
N. Moratelli, M. Barraco, D. Morelli, M. Cornia, L. Baraldi, and R. Cucchiara, “Fashion-oriented image caproning with external knowledge re-trieval and fully a|enrve gates,” Sensors, vol. 23, no. 3, pp. 1286, 2023, doi: 10.3390/s23031286.
G. Moro, S. Salvatori, and G. Frisoni, “Efficient text-image semanrc search: A mulr-modal vision-language approach for fashion retrieval,” Neurocompurng, vol. 538, Art. no. 126196, 2023, doi: 10.1016/j.neucom.2023.03.057.
S. S. Gurao and M. Parl, “Deconstruct to Reconstruct: Turning yesterday’s Clothes into Today’s Fashion,” Journal of Computaronal Analysis & Applicarons, vol. 33, no. 8, pp. 989, 2024.
J. Jin, S. A. Sharudin, and T. Wang, “Visual Design of Chinese Animated Films Infused with Oriental Punk Aesthercs,” Journal of Theory and Pracrce of Social Science, vol. 4, no. 04, pp. 1-8, 2024, doi: 10.53469/jtpss.2024.04(04).01.
M. Li, Q. Gao, and T. Yu, “Kappa starsrc considerarons in evaluarng inter-rater reliability between two raters: which, when and context ma|ers,” BMC cancer, vol. 23, no. 1, pp. 799, 2023, doi: 10.1186/s12885-023-11325-z.
G. Rau and Y. S. Shih, “Evaluaron of Cohen’s kappa and other measures of inter-rater agreement for genre analy-sis and other nominal data,” Journal of english for academic purposes, vol. 53, Art. no. 101026, 2021, doi: 10.1016/j.jeap.2021.101026.
N. Khodeir and F. Elghannam, “Efficient topic idenrficaron for urgent MOOC Forum posts using BERTopic and tradironal topic modeling techniques,” Educaron and Informaron Technologies, vol. 30, no. 5, pp. 5501-5527, 2025, doi: 10.1007/s10639-024-13003-4.
A. Rejeb, K. Rejeb, S. Simske, Süle, and E, “Industry 5.0 research: an approach using co-word analysis and BERTopic modeling,” Discover Sustainability, vol. 6, no. 1, pp. 402, 2025, doi: 10.1007/s43621-025-01252-3.
M. Herrmann, D. Kazempour, F. Scheipl, Kröger, and P, “Enhancing cluster analysis via topological manifold learning,” Data Mining and Knowledge Discovery, vol. 38, no. 3, pp. 840-887, 2024, doi: 10.1007/s10618-023-00980-2.
W. Yi, S. Bu, H. H. Lee, and C. H. Chan, “Compararve analysis of manifold learning-based dimension reducron methods: a mathemarcal perspecrve,” Mathemarcs, vol. 12, no. 15, pp. 2388, 2024, doi: 10.3390/math12152388.
G. Stewart and M. Al-Khassaweneh, “An implementaron of the HDBSCAN* clustering algorithm,” Applied Scienc-es, vol. 12, no. 5, pp. 2405, 2022, doi: 10.3390/app12052405.
S. M. Wang, W. R. Yang, Q. Y. Zhuang, W. H. Lin, M. Y. Tian, T. J. Su, et al., “Applicaron of Three-Dimensional Hierarchical Density-Based Sparal Clustering of Applicarons with Noise in Ship Automarc Idenrficaron System Trajectory-Cluster Analysis,” Applied Sciences, vol. 15, no. 5, pp. 2621, 2025, doi: 10.3390/app15052621.
M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” preprint arXiv, vol. 22, no. 03, Art. no. 05794, 2022, doi: 10.48550/arXiv.2203.05794.
W. Mu, K. H. Lim, J. Liu, S. Karunasekera, L. Falzon, and A. Harwood, “A clustering-based topic model using word networks and word embeddings,” Journal of big data, vol. 9, no. 1, pp. 38, 2022, doi: 10.1186/s40537-022-00585-4.
C. Tang, “Review on Applicaron of Chi-square Starsrc in Text Classificaron in Recent Five Years,” Applied and Computaronal Engineering, vol. 97, no. 1, pp. 115-118, 2024, doi: 10.54254/2755-2721/97/20241397.
D. C. Sami’Un, A. Sugiharto, and F. Jie, “Chi Square Feature Selecron For Improving Senrment Analysis of News Data Privacy Treats,” Journal of Theorercal and Applied Informaron Technology, vol. 102, no. 18, pp. 6601-6610, 2024.
W. Urasaki, T. Nakagawa, T. Momozaki, and S. Tomizawa, “Generalized Cramér’s coefficient via f-divergence for conrn-gency tables,” Advances in Data Analysis and Classificaron, vol. 18, no. 4, pp. 893-910, 2024, doi: 10.1007/s11634-023-00560-8.
R. L. Sapra and S. Saluja, “Understanding starsrcal associaron and correlaron,” Current Medicine Research and Pracrce, vol. 11, no. 1, pp. 31-38, 2021, doi: 10.4103/cmrp.cmrp_62_20.
C. Galli, N. Donos, and E. Calciolari, “Performance of 4 pre-trained sentence transformer models in the semanrc query of a systemarc review dataset on peri-implanrrs,” Informaron, vol. 15, no. 2, pp. 68, 2024, doi: 10.3390/info15020068.
G. B. Guedes and A. E. A. da Silva, “Classificaron and clustering of sentence-level embeddings of scienrfic arrcles generated by contrasrve learning,” preprint arXiv, vol. 24, no. 04, Art. no. 00224, 2024, doi: 10.48550/arXiv.2404.00224.
U. Chauhan and A. Shah, “Topic modeling using latent Dirichlet allocaron: A survey,” ACM Compurng Surveys (CSUR), vol. 54, no. 7, pp. 1-35, 2021, doi: 10.1145/3462478.
J. Gan, T. Liu, L. Li, and J. Zhang, “Non-negarve matrix factorizaron: a survey,” The Computer Journal, vol. 64, no. 7, pp. 1080-1092, 2021, doi: 10.1093/comjnl/bxab103.
G. Cheng, Q. You, L. Shi, Z. Wang, J. Luo, and T. Li, “A survey of topic models: From a whole-cycle perspecrve,” Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9929-9953, 2023, doi: 10.3233/JIFS-233551.