Abstract
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.
Highlights
Is article systematically summarizes the art work database commonly used in the current art work image research; based on the stroke characteristics, color characteristics, shape and texture characteristics, and white space characteristics of the art work image, it summarizes in detail the feature extraction techniques and features of different works of art
As can be seen from the figure, after using the sentiment dictionary designed in this article, compared with HowNet and NTUSD thesaurus, the accuracy, recall, and F-value have been significantly improved. e traditional HowNet sentiment dictionary does not grade the intensity of sentimental polarity of sentiment words
Because the number of words in the NTUSD thesaurus is insufficient to cover all of the emotional words in the data set, it performs slightly worse in terms of accuracy and recall than the emotional dictionary proposed in this article. e article uses a sentiment dictionary, SVM, Naive Bayes, and Logistic regression to test all of the sentences in the experimental data, with a training set of 4000 artificially labeled sentences. e goal of this study is to compare various art sentiment analysis algorithms in the field of art. e accuracy, recall, and Fvalue of the four types of artistic sentiment analysis algorithms in the current data set can be obtained more clearly using the advantages and disadvantages of sentiment analysis
Summary
Is article systematically summarizes the art work database commonly used in the current art work image research; based on the stroke characteristics, color characteristics, shape and texture characteristics, and white space characteristics of the art work image, it summarizes in detail the feature extraction techniques and features of different works of art. 4. Evaluation Method and Artistic Value Analysis Based on Art Emotion Classification Model
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