Research on the Art Value and Application of Art Creation Based on the Emotion Analysis of Art
Author(s) -
Dutao Wang
Publication year - 2022
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2022/2435361
Subject(s) - computer science , sentiment analysis , conditional random field , value (mathematics) , sentence , field (mathematics) , artificial intelligence , style (visual arts) , expression (computer science) , natural language processing , machine learning , visual arts , art , mathematics , pure mathematics , programming language
The ultimate embodiment of the value of the art creation process is artistic value, which is the embodiment of the greatest value created by art. Art creation is a form of art culture expression. To make their works more cultural and artistic, creators incorporate their personal creative style and ideological concepts. The ultimate expression of the value of the art creation process is artistic value, which is the embodiment of the greatest value of art creation. It provides a useful method for conducting digital research on human artistic works and has important implications for the protection and innovation of such works. In order to better realize artistic work research and innovation, this article primarily organizes and analyzes the literature on art classification and sentiment analysis currently available in the United States and abroad. This paper proposes a Python-based machine learning art emotion analysis method to investigate the issue of art emotion analysis. This program can achieve better results in analyzing sentiment orientation through a large number of experiments, and it is more efficient than a traditional weighted art sentiment analysis algorithm. This paper proposes a conditional random field extraction of core sentences-based art sentiment analysis algorithm for long works of art. The conditional random field is used to locate evaluation objects from which core sentences can be extracted, and an algorithm for sentiment sentence emotional polarity weight synthesis is proposed. Finally, experiments are used to compare the algorithm. The algorithm’s stability and effectiveness are demonstrated by its accuracy, recall, and F-value.
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