SENTIMENT ANALYSIS OF FOLK ART SOCIAL CAMPAIGNS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6867Keywords:
Folk Art, Sentiment Analysis, Social Campaigns, Cultural Communication, Emotional Engagemen, Deep Learning, BERT EmbeddingsAbstract [English]
Folk art is a deep method of social communication, which shows cultural identity, emotions of the community, and collective stories. The use of folk art in social campaigns is growing in the digital age as the means of triggering the feelings of empathy and reinforcing the awareness of the culture, although there is a lack of systemic assessment of the emotional response. The paper will investigate the combination of sentiment analysis strategies to assess the reactions of the public to folk art related social campaigns. An extensive database was assembled on social media and campaign archives of folk-art inspired textual and visual materials. To clean up textual data, preprocessing was done by means of tokenization, removal of stop words and normalization. Lexicon-based and machine learning models (SVM, Random Forest) and deep learning models (CNN, LSTM, and BERT) were used to classify sentiments. The higher order methods of feature extraction (TF-IDF, Word2vec and embedding BERT) were implemented with a view to augmenting semantic knowledge. The results of the analysis showed that there are high correlations between the cultural symbolism and emotional involvement, showing that folk motifs and regional idioms provoke more positive emotions than generic campaign designs. Results highlight the point that in addition to enhancing message resonance folk art can also be used to fill socio-cultural gaps by enhancing communication that is emotionally based.
References
Agüero-Torales, M. M., Salas, J. I. A., and López-Herrera, A. G. (2021). Deep Learning and Multilingual Sentiment Analysis on Social Media Data: An Overview. Applied Soft Computing, 107, 107373. https://doi.org/10.1016/j.asoc.2021.107373 DOI: https://doi.org/10.1016/j.asoc.2021.107373
Jang, H., Rempel, E., Roth, D., Carenini, G., and Janjua, N. Z. (2021). Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis. Journal of Medical Internet Research, 23, e25431. https://doi.org/10.2196/25431 DOI: https://doi.org/10.2196/25431
Keramatfar, A., and Amirkhani, H. (2019). Bibliometrics of Sentiment Analysis Literature. Journal of Information Science, 45, 3–15. https://doi.org/10.1177/0165551518793342 DOI: https://doi.org/10.1177/0165551518761013
Ligthart, A., Catal, C., and Tekinerdogan, B. (2021). Systematic Reviews in Sentiment Analysis: A Tertiary Study. Artificial Intelligence Review, 54, 4997–5053. https://doi.org/10.1007/s10462-020-09902-6 DOI: https://doi.org/10.1007/s10462-021-09973-3
Liu, B. (2022). Sentiment Analysis and Opinion Mining. Berlin, Germany: Springer Nature, 100–120.
Lu, C. (2024). A Review of the Research on the Protection Mechanism of China’s Intangible Cultural Heritage. Frontiers in Social Sciences, 13, 432–438. https://doi.org/10.3389/fsoc.2024.432 DOI: https://doi.org/10.12677/ass.2024.134319
Shen, R.-P., Liu, D., Wei, X., and Zhang, M. (2022). Your Posts Betray You: Detecting Influencer-Generated Sponsored Posts by Finding the Right Clues. Information & Management, 59, 103719. https://doi.org/10.1016/j.im.2022.103719 DOI: https://doi.org/10.1016/j.im.2022.103719
Wang, Z., Xie, Q., Feng, Y., Ding, Z., Yang, Z., and Xia, R. (2024). Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study. arXiv Preprint. arXiv:2304.04339
Wu, Y., Ngai, E. W., Wu, P., and Wu, C. (2020). Fake Online Reviews: Literature Review, Synthesis, and Directions for Future Research. Decision Support Systems, 132, 113280. https://doi.org/10.1016/j.dss.2020.113280 DOI: https://doi.org/10.1016/j.dss.2020.113280
Xing, F. (2024). Designing Heterogeneous LLM Agents for Financial Sentiment Analysis. arXiv Preprint. arXiv:2401.05799
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., and Chen, E. (2024). A Survey on Multimodal Large Language Models. arXiv Preprint. arXiv:2306.13549 DOI: https://doi.org/10.1093/nsr/nwae403
Zhang, W., Deng, Y., Liu, B., Pan, S., and Bing, L. (2023). Sentiment Analysis in the Era of Large Language Models: A Reality Check. arXiv Preprint. arXiv:2305.15005 DOI: https://doi.org/10.18653/v1/2024.findings-naacl.246
Zhang, W., Li, X., Deng, Y., Bing, L., and Lam, W. (2022). A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges. IEEE Transactions on Knowledge and Data Engineering, 35, 11019–11038. https://doi.org/10.1109/TKDE.2022.3148173 DOI: https://doi.org/10.1109/TKDE.2022.3230975
Zhang, X., Yang, D., Yow, C. H., Huang, L., Wu, X., Huang, X., Guo, J., Zhou, S., and Cai, Y. (2022). Metaverse for Cultural Heritages. Electronics, 11, 3730. https://doi.org/10.3390/electronics11203730 DOI: https://doi.org/10.3390/electronics11223730
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Girish Kalele, A R Chayapathi, Mohd Faisal, Ranjana Tiwari, Madhulika Srivastava, Dr. Badri Narayan Sahu, Ganesh Korwar
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
