Network analysis and sentiment classification of Minnan nursery rhymes
Source abstract
Abstract
Minnan nursery rhymes (MNRs), an integral part of Minnan intangible cultural heritage (ICH), are shared by southern Fujian, Taiwan, and overseas Chinese communities. Preserving and analyzing MNRs, especially their emotional evolution over time, is crucial but challenging due to shifting cultural contexts. Traditional sentiment analysis methods often overlook intrinsic relationships among nursery rhymes. To address this, we construct an MNR network using textual feature vectors and cosine similarity thresholds, enabling the exploration of structural and emotional patterns. Inspired by graph neural networks, we propose a Joint-GraphSAGE model for sentiment classification, which effectively captures complex relationships and semantic nuances. Comparative experiments with classical machine learning and deep learning models demonstrate that the Joint-GraphSAGE model significantly outperforms baselines in sentiment classification tasks for both traditional and modern MNRs. This approach not only enhances sentiment analysis accuracy for MNRs, but also offers new perspectives for studying ICH cultural connections.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Plant biochemistry and biosynthesis · Computational Drug Discovery Methods · Metabolomics and Mass Spectrometry Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
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1. Heritage, U. I. C. Texts of the convention for the safeguarding of the intangible cultural heritage. In Tillgänglig på Internet: https://ich. unesco. org/en/convention [hämtad Mars 2, 2020] J White, W. et al.(2018) Tabletop role-playing games. I Deterding, S. Zagal, J.(red.) Role-Playing Game Studies: Transmedia Foundations Routledge Taylor & Francis Group Østbye, H. et al. (2003) (Metodbok för Medievetenskap, Malmö, 2003).
2. Hui, L. The multi-cultural value of Minnan nursery rhymes and their contemporary transmission. Fujian Forum (Humanities and Social Sciences Edition) 125–128 (Universe Scientific Publishing, 2012).
3. Blake, J. Unesco’s 2003 convention on intangible cultural heritage. Intangible Heritage 45 (Routledge, 2008).
4. Chen, W. A Grammar of Southern Min: The Hui’an Dialect. (Mouton de Gruyter, Berlin, 2020).10.1515/9781501511868 ↗
5. Yanping, D. The linguistic excavation value of Minnan nursery rhymes. New Course (Comprehensive Edition) 10–11 (2019).
6. Wang, M. A new perspective on the inheritance of minnan nursery rhymes from the perspective of intangible cultural heritage. Contemp. Lit. Art. 6, 204–206 (2015).
7. Minnan Nursery Rhymes (accessed 10 December 2023; https://xiamen.chinadaily.com.cn/2019-05/06/c_346992.html (2019).
8. White, C. Waves of influence across the south seas: mutual support between protestants in minnan and southeast asia. Ching Feng 11, 29–54 (2012).
9. Junyu, R. The cultural value and transmission of minnan nursery rhymes in the context of intangible cultural heritage. J. Shenyang Norm. Univ. 37, 172–174 (2013).
10. Miao, W. Analysis of the contemporary cultural and educational value of minnan nursery rhymes. Literature & Art Criticism 4 (Universe Scientific Publishing, 2017).
11. Shunmei, L. & Zahari, Z. A. The design and development of cultural and creative products from the perspective of digital media-taking intangible cultural heritage of nursery rhymes in minnan dialect as an example. J. Posit. Sch. Psychol. 6, 287–298 (2022).
12. Network CICH. List of Representative Items of National Intangible Cultural Heritage. https://www.ihchina.cn/project.html (2008).
13. Thwaites, H. Digital heritage: What happens when we digitize everything? Visual Heritage in the Digital Age 327–348 (Springer, London, 2013).10.1007/978-1-4471-5535-5_17 ↗
14. Newell, J. Old objects, new media: Historical collections, digitization and affect. J. Mater. Cult. 17, 287–306 (2012).10.1177/1359183512453534 ↗
15. Yasser, A., Clawson, K., Bowerman, C., Lévêque, M. et al. Saving cultural heritage with digital make-believe: machine learning and digital techniques to the rescue. In HCI’17 Proc 31st British Computer Society Human Computer Interaction Conference, Vol. 97, 1–5 (ACM, 2017).10.14236/ewic/HCI2017.97 ↗
16. Chen, Z., Huang, J., Dai, H. & Liu, J. Development route analysis of intangible cultural heritage industry of china based on data mining. In Journal of Physics: Conference Series, Vol. 1848, 012040 (IOP Publishing, 2021).10.1088/1742-6596/1848/1/012040 ↗
17. Dou, J., Qin, J., Jin, Z. & Li, Z. Knowledge graph based on domain ontology and natural language processing technology for chinese intangible cultural heritage. J. Vis. Lang. Comput. 48, 19–28 (2018).10.1016/j.jvlc.2018.06.005 ↗
18. Pang, B. & Lee, L. et al. Opinion mining and sentiment analysis. Found. Trends® Inf. Retr. 2, 1–135 (2008).10.1561/1500000011 ↗
19. Hemmatian, F. & Sohrabi, M. K. A survey on classification techniques for opinion mining and sentiment analysis. Artif. Intell. Rev. 52, 1495–1545 (2019).10.1007/s10462-017-9599-6 ↗
20. Sadia, A., Khan, F. & Bashir, F. An overview of lexicon-based approach for sentiment analysis. In 2018 3rd International Electrical Engineering Conference 1–6 (IEEC, 2018).
21. Patel, S. N. & Choksi, M. J. B. A survey of sentiment classification techniques. J. Res. 3, 2347–3878 (2015).10.70729/J2013478 ↗
22. Zhang, H., Gan, W. & Jiang, B. Machine learning and lexicon based methods for sentiment classification: A survey. In 2014 11th web information system and application conference 262–265 (IEEE, 2014).10.1109/WISA.2014.55 ↗
23. Zhang, L., Wang, S. & Liu, B. Deep learning for sentiment analysis: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8, e1253 (2018).10.1002/widm.1253 ↗
24. Do, H. H., Prasad, P., Maag, A. & Alsadoon, A. Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst. Appl. 118, 272–299 (2019).10.1016/j.eswa.2018.10.003 ↗
25. Ayetiran, E. F. Attention-based aspect sentiment classification using enhanced learning through cnn-bilstm networks. Knowl.-Based Syst. 252, 109409 (2022).10.1016/j.knosys.2022.109409 ↗
26. Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR, 2017).
27. Velickovic, P. et al. Graph attention networks. stat 1050, 10–48550 (2017).
28. Xiao, L. et al. Targeted sentiment classification based on attentional encoding and graph convolutional networks. Appl. Sci. 10, 957 (2020).10.3390/app10030957 ↗
29. Li, Y. & Li, N. Sentiment analysis of Weibo comments based on graph neural network. IEEE Access 10, 23497–23510 (2022).10.1109/ACCESS.2022.3154107 ↗
30. Lu, G., Li, J. & Wei, J. Aspect sentiment analysis with heterogeneous graph neural networks. Inf. Process. Manag. 59, 102953 (2022).10.1016/j.ipm.2022.102953 ↗
31. Wang, M. & Hu, G. A novel method for twitter sentiment analysis based on attentional-graph neural network. Information 11, 92 (2020).10.3390/info11020092 ↗
32. Phan, H. T., Nguyen, N. T. & Hwang, D. Aspect-level sentiment analysis: a survey of graph convolutional network methods. Inf. Fusion 91, 149–172 (2023).10.1016/j.inffus.2022.10.004 ↗
33. Jin, Z., Tao, M., Zhao, X. & Hu, Y. Social media sentiment analysis based on dependency graph and co-occurrence graph. Cogn. Comput. 14, 1039–1054 (2022).10.1007/s12559-022-10004-8 ↗
34. Mullen, T. & Collier, N. Sentiment analysis using support vector machines with diverse information sources. In Proc. 2004 Conference on Empirical Methods in Natural Language Processing 412–418 (Association for Computational Linguistics, Barcelona, 2004).
35. Prabhat, A. & Khullar, V. Sentiment classification on big data using naiuml;ve bayes and logistic regression. In 2017 International Conference on Computer Communication and Informatics (ICCCI), 1–5 (IEEE, 2017).10.1109/ICCCI.2017.8117734 ↗
36. Dos Santos, C. & Gatti, M. Deep convolutional neural networks for sentiment analysis of short texts. In Proc COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers 69–78 (Dublin City University and Association for Computational Linguistics, Dublin, 2014).
37. Li, D. & Qian, J. Text sentiment analysis based on long short-term memory. In 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI) 471–475 (IEEE, 2016).10.1109/CCI.2016.7778967 ↗
38. Zhang, K., Song, W., Liu, L., Zhao, X. & Du, C. Bidirectional long short-term memory for sentiment analysis of chinese product reviews. In 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC) 1–4 (IEEE, 2019).10.1109/ICEIEC.2019.8784560 ↗
39. Hamilton, W., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems (NIPS, Long Beach, 2017).