Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Abstract

Video artworks are closely linked with the development of contemporary technology. Therefore, it is widely used in various fields of social life. Video art has become one of the main media forms of contemporary art. In the practice of art teaching, how to combine the existing content of traditional art teaching with video technology and how to understand the inner connection between traditional aesthetics and technological aesthetics have become issues that workers in the new era must think about and pay attention to. As a typical case of influencing works of art, movie animation is loved by the majority of young people. In order to quantify the application effect of machine learning in video art and film animation text mining, this paper conducts prediction research and analysis on several main aspects of color features involved in film animation. By introducing three typical machine learning methods, this paper analyzes the distribution law of the color features of film animation from the perspective of machine learning and its influence on artistic texts. Specifically, the paper uses machine learning methods as a carrier to predict the performance of multiple main modules of color features in movie animation. The prediction results show that the square of the correlation coefficient corresponding to the extreme learning machine is the largest, and the root mean square error, the mean absolute percentage error, and the median absolute error are the smallest, which shows that the extreme learning machine has the best prediction effect. Therefore, it corresponds to the best prediction. In addition, the comparison between the predicted data and the measured data shows that the relationship between the two is approximately a linear function of y = x. At the same time, the fitting calculation shows that the predicted data corresponding to the two main modules of the main color and the color structure in the color feature exhibit a good functional relationship of polynomial functions.