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

<p>In the context of the information age, mining for emotions has become a popular topic today, and deep learning plays an important role in the task of sentiment analysis. In this study, we propose a LE-CNN-MBiLSTM fine-grained sentiment analysis model for the task of fine-grained sentiment computation for emotional expression in visual communication design. The model combines the ERNIE model and introduces a parallel CNN and dual-channel BiLSTM structure, which first mines multiple local key features in the text with CNN, then extracts the contextual semantics with BiLSTM, and extracts the fusion features by CNN-BiLSTM. Fine-grained sentiment analysis tasks are well-served by the model in this paper, with an accuracy of 93.58% and a loss function value of 0.18, respectively. Using the model to analyze the corpus of comments on visual communication design works, positive and negative emotions dominated the samples, each accounting for 50%, and the expression of sadness was particularly prominent. The model in this paper can be applied in fine-grained sentiment computation for visual communication design and can be migrated to other natural language processing domains, thus providing a new idea for the construction of network models for text sentiment analysis.</p>
</abstract>

In the context of the information age, mining for emotions has become a popular topic today, and deep learning plays an important role in the task of sentiment analysis. In this study, we propose a LE-CNN-MBiLSTM fine-grained sentiment analysis model for the task of fine-grained sentiment computation for emotional expression in visual communication design. The model combines the ERNIE model and introduces a parallel CNN and dual-channel BiLSTM structure, which first mines multiple local key features in the text with CNN, then extracts the contextual semantics with BiLSTM, and extracts the fusion features by CNN-BiLSTM. Fine-grained sentiment analysis tasks are well-served by the model in this paper, with an accuracy of 93.58% and a loss function value of 0.18, respectively. Using the model to analyze the corpus of comments on visual communication design works, positive and negative emotions dominated the samples, each accounting for 50%, and the expression of sadness was particularly prominent. The model in this paper can be applied in fine-grained sentiment computation for visual communication design and can be migrated to other natural language processing domains, thus providing a new idea for the construction of network models for text sentiment analysis.

Fine-Grained Emotional Calculation of Emotional Expression in Modern Visual Communication Designs

Applied Mathematics and Nonlinear Sciences

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

In the context of the information age, mining for emotions has become a popular topic today, and deep learning plays an important role in the task of sentiment...

Fine-Grained Emotional Calculation of Emotional Expression in Modern Visual Communication Designs

Data publikacji: 03 cze 2024

Zakres stron: -

Otrzymano: 01 mar 2024

Przyjęty: 07 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1309

Słowa kluczowe
ERNIE, CNN, BiLSTM, Fine-grained sentiment analysis, Visual communication design

© 2024 Yimiao Zhang, et al., published by Sciendo

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

Fine-Grained Emotional Calculation of Emotional Expression in Modern Visual Communication Designs

Data publikacji: 03 cze 2024

Zakres stron: -

Otrzymano: 01 mar 2024

Przyjęty: 07 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1309

Słowa kluczoweERNIE, CNN, BiLSTM, Fine-grained sentiment analysis, Visual communication design

© 2024 Yimiao Zhang, et al., published by Sciendo

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

Słowa kluczowe
ERNIE, CNN, BiLSTM, Fine-grained sentiment analysis, Visual communication design