Multiple Linear Regression Analysis of the Animation Score
DOI:
https://doi.org/10.54097/hset.v49i.8501Keywords:
Multiple linear regression models; animation ratings; China's animation industry development.Abstract
With the development of China’s economy, people’s living standards continue improving. This makes the cultural industry flourished. As an important part of the cultural, the animation industry is entering a golden age of development. The rapid development of the animation industry has led to a large number of national comics of varying quality. Based on the cultural atmosphere of domestic animation is not strong, the originality of animation production is not strong, the animation industry chain needs to be improved and other problems. This paper analyzed the significant factors affecting animation ratings through multiple linear regression models to make some suggestions for the future development of domestic animation. The summary analysis shows that name, storyboard and protagonist have a significant positive influence on the score. From the results, China's animation industry should pay attention to plot development and character building. It is hoped that the findings of this paper can have some guiding significance for the development of national comics.
Downloads
References
Chengsen Chi, Yuhua Zhang, Anzhi Wang. China's animation industry development prospects Research. Technology and Economy Market2020, 8: 145-147.
Sifan Zhang. The current situation and suggestions for the development of China's animation industry. Shanghai Business, 2022, 17-19.
Zixuan Yang. Analysis of the development and current situation of Chinese animation. World of News, 2015, 76-80.
Ning Kuang. Experimental discussion on the development of China's animation industry status and countermeasures. Cradle of Journalists, 2021, 31-32.
Ling Li. Japanese manga in power-the development of Chinese animation should learn from the experience. Young Literary Artists, 2016, 155.
Gang Lei, Lu Liu, Changhui Zhou. An Empirical Study on the Effect of Online Movie Ratings on Box Office. Film Art, 2019, 146-153.
Juan Geng, Mingxing Guo. Douban Top 250 movie data mining and rating prediction. Hebei Enterprises, 2021, 2: 11-13.
Xiangjun Li, Xiaoling Xiao. A study on movie rating prediction based on machine learning. Computer Knowledge and Technology: Academic Edition, 2021.
Rubiao Zhou, Xiaoxia Lin, Yihua Wang. Multiple regression-based analysis of Douban movie ratings. Art Technology, 2019, 32(01): 67-68.
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2019.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







