<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Deep learning technology has the characteristics of automatic learning, nonlinear modeling, etc., which makes the machine algorithm better adapt to the data so as to better complete the task of sentiment analysis. The purpose of this paper is to explore the application of deep learning technology in the emotional analysis of art design works. After combining the emotion construction mechanism of art design works, based on the superior performance of convolutional neural network image recognition, it is introduced into the emotion analysis of art design works, combined with the attention mechanism model, to construct the emotion analysis model of art design works based on deep learning. Art design works are collected as the experimental dataset. The emotion analysis effect of this paper’s model for art design works is evaluated through the comparison of emotion classification indexes. The comparison of ROC curves of different models, and the emotion instance analysis of art design works is carried out. The experiments show that the experimental accuracy, precision, recall, and F1 value of this paper’s model range from 81% to 86%, and the F1 and ROC values are improved by 2.3% to 7.0% and 1.7% to 4.2% compared with the comparison model, which reflects the high accuracy and precision of this paper’s model for the sentiment analysis of art design works. Seven emotional patterns were found in tea art design works, and the model can be used to analyze the emotions of art design works.</p>
</abstract>

Deep learning technology has the characteristics of automatic learning, nonlinear modeling, etc., which makes the machine algorithm better adapt to the data so as to better complete the task of sentiment analysis. The purpose of this paper is to explore the application of deep learning technology in the emotional analysis of art design works. After combining the emotion construction mechanism of art design works, based on the superior performance of convolutional neural network image recognition, it is introduced into the emotion analysis of art design works, combined with the attention mechanism model, to construct the emotion analysis model of art design works based on deep learning. Art design works are collected as the experimental dataset. The emotion analysis effect of this paper’s model for art design works is evaluated through the comparison of emotion classification indexes. The comparison of ROC curves of different models, and the emotion instance analysis of art design works is carried out. The experiments show that the experimental accuracy, precision, recall, and F1 value of this paper’s model range from 81% to 86%, and the F1 and ROC values are improved by 2.3% to 7.0% and 1.7% to 4.2% compared with the comparison model, which reflects the high accuracy and precision of this paper’s model for the sentiment analysis of art design works. Seven emotional patterns were found in tea art design works, and the model can be used to analyze the emotions of art design works.

Sentiment analysis of art and design works using deep learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Sentiment analysis of art and design works using deep learning"}

Deep learning technology has the characteristics of automatic learning, nonlinear modeling, etc., which makes the machine algorithm better adapt to the data so...

Sentiment analysis of art and design works using deep learning

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 02 abr 2024

Aceptado: 22 jun 2024

DOI: https://doi.org/10.2478/amns-2024-2193

Palabras clave
Deep learning, Convolutional neural network, Attention mechanism, Sentiment analysis, Art and design works

© 2024 Zhonghua Dang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Sentiment analysis of art and design works using deep learning

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 02 abr 2024

Aceptado: 22 jun 2024

DOI: https://doi.org/10.2478/amns-2024-2193

Palabras claveDeep learning, Convolutional neural network, Attention mechanism, Sentiment analysis, Art and design works

© 2024 Zhonghua Dang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Deep learning, Convolutional neural network, Attention mechanism, Sentiment analysis, Art and design works