Application of Interactive Data Visualization Design Based on BERT Sentiment Analysis in Patriotism Education
Main Article Content
Abstract
Complex cultural metaphors in patriotism education texts make accurate sentiment analysis difficult, while existing visualization tools provide limited interactivity for instructional support. To address these limitations, this study develops an interactive data visualization framework driven by a domain-enhanced BERT model for high-metaphor and highcompliance educational scenarios. Domain-adaptive pre-training and Fast Gradient Method adversarial training are introduced to strengthen semantic representation learning, enabling the model to identify implicit rhetorical expressions rather than relying on surface lexical features. The optimized DE-BERT model recognizes domain-specific metaphors, euphemisms, and latent value orientations, providing interpretable semantic evidence for educational analysis. Guided by cognitive load theory, a three-level visualization architecture integrating smoothed Bézier sentiment curves, dynamic heatmap arrays, and repulsive bubble labels is constructed to improve information presentation and user interaction. The framework also offers methodological value for semantic perception and intelligent visualization in electromagnetic information processing and human– machine collaborative analysis. Experiments show a classification accuracy of 0.90 and macro F1-score of 0.88 under high-metaphor-density conditions, with response times of 783.4 ms, 186.3 ms, and 874.6 ms across teaching scenarios and a single-operation success rate above 97%.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Y. Wang, “An Analysis of the New Path of Patriotic Education in Ideological and Political Courses in Universities,” Journal of Theory and Practice of Social Science, vol. 3, no. 7, pp. 7-10, 2023.
S. Malkoç and F. Ö zturk, “A Comparative Review of Articles on Education of Patriotism: A Thematic Analysis,” International Journal of Progressive Education, vol. 17, no. 6, pp. 144-157, 2021, doi: 10.29329/ijpe.2021.382.10.
C. Long and J. Li, “Analysis and international comparison of the change of patriotic education emotion and behavior in the era of self media,” International Journal of Neuropsychopharmacology, vol. 25, no. Supplement_1, pp. A75-A76, 2022, doi: 10.1093/ijnp/pyac032.103.
D. A. Setiyanto, “Promoting patriotism and religious moderation: a critical discourse analysis of sanadmedia,” Com. Harmoni, vol. 23, no. 1, pp. 53-67, 2024, doi: 10.32488/harmoni.v23i1.716.
Z. Huang, Z. Yang, and T. Meng, “National identity of locality: The state, patriotism, and nationalism in cyber China,” Journal of Chinese Political Science, vol. 28, no. 1, pp. 51-83, 2023, doi: 10.1007/s11366-022-09820-4.
C. Long and G. Yang, “Study on patriotic education and changes of patriotic emotion and behavior of ethnic minority college students,” International Journal of Neuropsychopharmacology, vol. 25, no. Supplement_1, pp. A74-A75, 2022, doi: 10.1093/ijnp/pyac032.102.
J. C. Lin and L. Jackson, “Patriotism in moral education: Toward a rational approach in China,” Journal of Moral Education, vol. 52, no. 3, pp. 343-361, 2023, doi: 10.1080/03057240.2022.2136148.
K. Fukuoka and S. Takita-Ishii, “Teaching how to love your country in schools?: A study of Japanese youth narratives on patriotic education,” National Identities, vol. 24, no. 3, pp. 247-270, 2022, doi: 10.1080/14608944.2021.1931084.
J. Cui and Q. Tong, “How does the Chinese Government conduct emotional governance over COVID-19? Content analysis of video blogs,” Chinese Journal of Communication, vol. 16, no. 2, pp. 150-167, 2023, doi: 10.1080/17544750.2022.2125024.
K. S. Bakhtiyarovna, “Pedagogical conditions for acquaintance of preschool children with monuments of national architecture in the education of patriotism,” International Journal of Social Science & Interdisciplinary Research, vol. 11, no. 05, pp. 119-125, 2022.
Q. Zhang and C. J. Wang, “Chinese online nationalism as imaginary engagement: an automated sentiment analysis of Tencent news comments on the 2012 Diaoyu (Senkaku) Islands incident,” Humanities and Social Sciences Communications, vol. 11, no. 1, pp. 1-11, 2024, doi: 10.1057/s41599-024-02983-w.
D. Chekmenev, E. Efimova, and E. Davydova, “The structure of patriotism as a universal value in the system of value orientations of Russian students and its practical projections,” Journal of Sociology: Bulletin of Yerevan University, vol. 15, no. 1 (39), pp. 15-27, 2024, doi: 10.46991/BYSU:F/2024.15.1.015.
H. Zhao, L. Wang, and S. Liu, “Research on the strategy of integrating red culture into patriotic education activities of Young People,” Open Journal of Applied Sciences, vol. 13, no. 7, pp. 1178-1195, 2023, doi: 10.4236/ojapps.2023.137093.
P. Dirksmeier, “The relationship between patriotism and regional identification: a cross-country analysis,” The Annals of Regional Science, vol. 71, no. 2, pp. 343-362, 2023, doi: 10.1007/s00168-022-01167-1.
X. Liu, X. Zhao, and H. Starkey, “Ideological and political education in Chinese Universities: structures and practices,” Asia Pacific Journal of Education, vol. 43, no. 2, pp. 586-598, 2023, doi: 10.1080/02188791.2021.1960484.
D. Gruntkowska, “Threats related to patriotic education,” An analysis of the primary school textbooks. Kultura i Edukacja, vol. 138, no. 4, pp. 216-233, 2022.
Y. Wu, “The fight metaphor in translation: From patriotism to pragmatism: A corpus-based critical analysis of metaphor in China’s political discourse,” Target, vol. 36, no. 1, pp. 50-75, 2024, doi: 10.1075/target.21151.wu.
Z. Yu and T. Hirzel, “Museum text translation in the Chinese context: the museum role and text production,” Museum Management and Curatorship, vol. 39, no. 2, pp. 188-202, 2024, doi: 10.1080/09647775.2022.2132992.
X. Wu and R. Fitzgerald, “Internet memes and the mobilization of a “One-China” cyber nationalist campaign: the case of the 2016 Diba Expedition to Taiwan,” Social Semiotics, vol. 34, no. 4, pp. 697-719, 2024, doi: 10.1080/10350330.2023.2183113.
F. Guo, X. Huang, W. Hu, X. Tong, and L. Liang, “A Comparative analysis of multimodal discourse on Chinese and international memorial museum official websites: A visual grammar perspective,” Journal of Humanities and Social Sciences Studies, vol. 7, no. 7, pp. 65-84, 2025, doi: 10.32996/jhsss.2025.7.7.9.
B. Yin, H. Wu, and W. Kong, “A joint event extraction model based on RoBERTa-wwm-ext and gating mechanism,” Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 4, pp. 2101-2112, 2023, doi: 10.3233/JCM-226772.
Y. Zhang, Y. Wang, and B. Li, “Named Entity Recognition in Chinese Electronic Medical Records Based on RoBERTa-wwm Dynamic Fusion Mode,” Data Analysis and Knowledge Discovery, vol. 6, no. 2, pp. 242-250, 2022, doi: 10.11925/infotech.2096-3467.2021.0951.
C. Wang and F. Feng, “ERNIE based intelligent triage system,” Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5013-5022, 2022, doi: 10.3233/JIFS-212140.
X. Wu and Y. Zhang, “Research on the application of ERNIE bot in college english teaching,” International Journal of Education and Humanities, vol. 12, no. 3, pp. 216-219, 2024, doi: 10.54097/ynbb1n08.
D. Shi, Z. Li, J. Zurada, A. Manikas, J. Guan, and P. Weichbroth, “Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents,” Knowledge and information systems, vol. 66, no. 4, pp. 2651-2681, 2024, doi: 10.1007/s10115-023-02036-9.
X. Shi, M. Hu, F. Ren, and P. Shi, “A dual-ways feature fusion mechanism enhancing active learning based on TextCNN,” Intelligent Data Analysis, vol. 28, no. 5, pp. 1189-1211, 2024, doi: 10.3233/IDA-230332.
Y. Qiu, C. Fang, S. Song, X. Huang, C. Wang, and J. Wang, “TsQuality: Measuring Time Series Data Quality in Apache IoTDB,” Proceedings of the VLDB Endowment, vol. 16, no. 12, pp. 3982-3985, 2023, doi: 10.14778/3611540.3611601.
M. Ariandi and S. R. Puteri, “Analisis visualisasi data kecamatan kertapati menggunakan tableau public,” JUPITER (Jurnal Penelit. Ilmu dan Tek. Komputer), vol. 14, no. 2, pp. 366-373, 2022.
A. R. Krishnan, “Research trends in criteria importance through intercriteria correlation (CRITIC) method: a visual analysis of bibliographic data using the Tableau software,” Information Discovery and Delivery, vol. 53, no. 2, pp. 233-247, 2025, doi: 10.1108/IDD-02-2024-0030.
G. Shoaib and S. Nandi, “Power BI dashboard for data analysis,” International Research Journal of Engineering and Technology, vol. 9, no. 07, pp. 2395-0056, 2022.
R. A. D. Vásquez, J. L. A. Espinoza, and M. A. C. Cabrera, “Power bi como herramienta de apoyo a la toma de decisiones,” Universidad y Sociedad, vol. 14, no. S3, pp. 195-207, 2022, [Online]. Available: https://rus.ucf.edu.cu/index.php/rus/article/view/2949.
J. Wang and K. Wang, “Bert-based semi-supervised domain adaptation for disastrous classification,” Multimedia Systems, vol. 28, no. 6, pp. 2237-2246, 2022, doi: 10.1007/s00530-022-00956-0.
M. Ryu, G. Lee, and K. Lee, “Knowledge distillation for bert unsupervised domain adaptation,” Knowledge and Information Systems, vol. 64, no. 11, pp. 3113-3128, 2022, doi: 10.1007/s10115-022-01736-y.
W. Zhang, C. Dong, J. Yin, and J. Wang, “Attentive representation learning with adversarial training for short text clustering,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 11, pp. 5196-5210, 2021, doi: 10.1109/TKDE.2021.3052244.
W. Wang, R. Wang, L. Wang, Z. Wang, and A. Ye, “Towards a robust deep neural network against adversarial texts: A survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 3, pp. 3159-3179, 2021, doi: 10.1109/TKDE.2021.3117608.
N. Wenk, J. Penalver-Andres, K. A. Buetler, T. Nef, R. M. Müri, and L. Marchal-Crespo, “Effect of immersive visualization technologies on cognitive load, motivation, usability, and embodiment,” Virtual Reality, vol. 27, no. 1, pp. 307-331, 2023, doi: 10.1007/s10055-021-00565-8.