Sentiment analysis of art and design works using deep learning

[1] Prabha, M. I., & Srikanth, G. U. (2019, April). Survey of sentiment analysis using deep learning techniques. In 2019 1st international conference on innovations in information and communication technology (ICIICT) (pp. 1-9). IEEE. Search in Google Scholar

[2] Dang, C. N., Moreno-Garcia, M. N., & De la Prieta, F. (2021). Hybrid deep learning models for sentiment analysis. Complexity, 2021(1), 9986920. Search in Google Scholar

[3] Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architectures. electronics, 8(3), 292. Search in Google Scholar

[4] Han, X. F., Laga, H., & Bennamoun, M. (2019). Image-based 3D object reconstruction: State-of-the-art and trends in the deep learning era. IEEE transactions on pattern analysis and machine intelligence, 43(5), 1578-1604. Search in Google Scholar

[5] Lučić, M., Tschannen, M., Ritter, M., Zhai, X., Bachem, O., & Gelly, S. (2019, May). High-fidelity image generation with fewer labels. In International conference on machine learning (pp. 4183-4192). PMLR. Search in Google Scholar

[6] Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., & Cohen-Or, D. (2021). Encoding in style: a stylegan encoder for image-to-image translation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2287-2296). Search in Google Scholar

[7] Audry, S. (2021). Art in the age of machine learning. Mit Press. Search in Google Scholar

[8] Mazzone, M., & Elgammal, A. (2019, February). Art, creativity, and the potential of artificial intelligence. In Arts (Vol. 8, No. 1, p. 26). MDPI. Search in Google Scholar

[9] Cetinic, E., & She, J. (2022). Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(2), 1-22. Search in Google Scholar

[10] Marks, J., Andalman, B., Beardsley, P. A., Freeman, W., Gibson, S., Hodgins, J., ... & Shieber, S. (2023). Design galleries: A general approach to setting parameters for computer graphics and animation. In Seminal Graphics Papers: Pushing the Boundaries, Volume 2 (pp. 73-84). Search in Google Scholar

[11] Franco, F. (2017). Generative Systems Art: The Work of Ernest Edmonds. Routledge. Search in Google Scholar

[12] Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53(6), 4335-4385. Search in Google Scholar

[13] Do, H. H., Prasad, P. W., Maag, A., & Alsadoon, A. (2019). Deep learning for aspect-based sentiment analysis: a comparative review. Expert systems with applications, 118, 272-299. Search in Google Scholar

[14] Chiu, M. C., Hwang, G. J., Hsia, L. H., & Shyu, F. M. (2024). Artificial intelligence-supported art education: A deep learning-based system for promoting university students’ artwork appreciation and painting outcomes. Interactive Learning Environments, 32(3), 824-842. Search in Google Scholar

[15] Mikołajczyk, A., & Grochowski, M. (2018, May). Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117-122). IEEE. Search in Google Scholar

[16] Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018). Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1316-1324). Search in Google Scholar

[17] Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48. Search in Google Scholar

[18] Zhang, H., Xu, H., Tian, X., Jiang, J., & Ma, J. (2021). Image fusion meets deep learning: A survey and perspective. Information Fusion, 76, 323-336. Search in Google Scholar