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

<p>This study focuses on the public’s perception of a specific country’s image, and adopts comment texts from multimodal translated materials as the object of analysis. A country image text perception model was established by combining the TextRank algorithm and the LDA topic model. The model extracts and categorizes themes from the collected comment data, and discusses in depth the different themes of country image and their degree of attention. It is found that the most popular theme is “economic dimension”, with a heat degree of 42.59%, followed by “regional development” and “political dimension”. This reflects the high level of public interest in these areas. In addition, the study quantified the public’s emotional disposition towards the country’s image and found that most people have a positive attitude towards the country, with a high percentage of positive sentiment of 84.89%, but most of them are mildly positive. This suggests that although the public’s overall perception tends to be positive, there is room to improve the country’s image and enhance the public’s positive perception.</p>
</abstract>

This study focuses on the public’s perception of a specific country’s image, and adopts comment texts from multimodal translated materials as the object of analysis. A country image text perception model was established by combining the TextRank algorithm and the LDA topic model. The model extracts and categorizes themes from the collected comment data, and discusses in depth the different themes of country image and their degree of attention. It is found that the most popular theme is “economic dimension”, with a heat degree of 42.59%, followed by “regional development” and “political dimension”. This reflects the high level of public interest in these areas. In addition, the study quantified the public’s emotional disposition towards the country’s image and found that most people have a positive attitude towards the country, with a high percentage of positive sentiment of 84.89%, but most of them are mildly positive. This suggests that although the public’s overall perception tends to be positive, there is room to improve the country’s image and enhance the public’s positive perception.

National Image Construction Based on Multimodal Translation in the Context of Big Data

Applied Mathematics and Nonlinear Sciences

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

This study focuses on the public’s perception of a specific country’s image, and adopts comment texts from multimodal translated materials as the object of...

National Image Construction Based on Multimodal Translation in the Context of Big Data

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 15. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0504

Schlüsselwörter
TextRank algorithm, LDA themes, Public perception, Country image

© 2024 Lun Wang, published by Sciendo

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

National Image Construction Based on Multimodal Translation in the Context of Big Data

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 15. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0504

SchlüsselwörterTextRank algorithm, LDA themes, Public perception, Country image

© 2024 Lun Wang, published by Sciendo

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

Schlüsselwörter
TextRank algorithm, LDA themes, Public perception, Country image