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

<p>The development of contemporary mobile Internet and new media has ushered in a new paradigm of narrative and communication forms. This paper takes the performance of narrative thinking as an entry point and establishes a framework for narrative thinking design by combining the narrative design process and digital technology. Narrative theme analysis is carried out in text narrative and image narrative. The text narrative is characterized by the LDA theme model for extracting narrative theme features. Then the LSTM model is used to classify the emotion of the extracted narrative theme. The visual features of an image narrative are extracted by a self-attention mechanism, combined with a Net VLAD algorithm for feature aggregation, and a compression excitation context gating unit and classifier are introduced to achieve sentiment classification. For the application of narrative thinking design, the news event of MH370 and the elementary school students of A elementary school in S city are taken as the research objects to explore the content and illustration of narrative thinking. The study shows that narrative thinking design can clarify the news event’s specific emotional expression and visualization illustration. The coefficient of the dream narrative theme is 0.317 in the elementary school students’ narrative theme change. The score of the positive emotion is 0.349±0.205, which is 0.024 points lower than the negative emotion. Using narrative thinking to analyze the content and illustrations can identify specific changes, thus improving the corresponding narrative design.</p>
</abstract>

The development of contemporary mobile Internet and new media has ushered in a new paradigm of narrative and communication forms. This paper takes the performance of narrative thinking as an entry point and establishes a framework for narrative thinking design by combining the narrative design process and digital technology. Narrative theme analysis is carried out in text narrative and image narrative. The text narrative is characterized by the LDA theme model for extracting narrative theme features. Then the LSTM model is used to classify the emotion of the extracted narrative theme. The visual features of an image narrative are extracted by a self-attention mechanism, combined with a Net VLAD algorithm for feature aggregation, and a compression excitation context gating unit and classifier are introduced to achieve sentiment classification. For the application of narrative thinking design, the news event of MH370 and the elementary school students of A elementary school in S city are taken as the research objects to explore the content and illustration of narrative thinking. The study shows that narrative thinking design can clarify the news event’s specific emotional expression and visualization illustration. The coefficient of the dream narrative theme is 0.317 in the elementary school students’ narrative theme change. The score of the positive emotion is 0.349±0.205, which is 0.024 points lower than the negative emotion. Using narrative thinking to analyze the content and illustrations can identify specific changes, thus improving the corresponding narrative design.

Narrative Thinking Oriented Content and Diagram

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Narrative Thinking Oriented Content and Diagram"}

The development of contemporary mobile Internet and new media has ushered in a new paradigm of narrative and communication forms. This paper takes the...

Narrative Thinking Oriented Content and Diagram

Publié en ligne: 03 mai 2024

Pages: -

Reçu: 17 avr. 2024

Accepté: 24 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1012

Mots clés
LDA topic model, LSTM model, Self-attention mechanism, Net VLAD algorithm, Narrative thinking

© 2024 Lei Guo et al., published by Sciendo

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

Narrative Thinking Oriented Content and Diagram

Publié en ligne: 03 mai 2024

Pages: -

Reçu: 17 avr. 2024

Accepté: 24 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1012

Mots clésLDA topic model, LSTM model, Self-attention mechanism, Net VLAD algorithm, Narrative thinking

© 2024 Lei Guo et al., published by Sciendo

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

Mots clés
LDA topic model, LSTM model, Self-attention mechanism, Net VLAD algorithm, Narrative thinking