Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis

Chen Wei-feng; Su Lei

doi:doi:10.11648/j.si.20231106.16

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Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis

Chen Wei-feng, Su Lei^*

Published in Science Innovation (Volume 11, Issue 6)

Received: 22 November 2023 Accepted: 22 November 2023 Published: 29 November 2023

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Abstract

Traditional information dissemination channels can no longer meet the needs of tourists for in-depth cultural tourism experience. More and more tourists hope to make their own tourism plans through the feedback from other tourists, and management departments also need to make strategies for scenic spot optimization by referring to the feedback from scenic spot experience. This study uses word frequency statistics and sentiment analysis based on deep learning to evaluate the perception of aesthetic, cultural and service values of Sha Mian Island scenic spot, using image semantic cutting to perceive the tendency of architectural photography, discover the shortcomings of the scenic spot and give suggestions for optimisation. The results show that the aesthetic value and greenery level of Sha Mian Island is high, the scenic content is vague, and the image data is not ideal for the perception of human and service content. This study provides a way of research that has a wide range of data sources, is easy to operate and can be quickly calculated and analysed in a short period of time with time-sensitive evaluations and pictures, giving a way of research that provides optimisation solutions for tourist attractions.

Published in	Science Innovation (Volume 11, Issue 6)
DOI	10.11648/j.si.20231106.16
Page(s)	259-265
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Image Semantic, Tourism Perception, Word Frequency Analysis, Sentiment Analysis, Semantic Network Analysis

References

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APA Style

Wei-feng, C., Lei, S. (2023). Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Science Innovation, 11(6), 259-265. https://doi.org/10.11648/j.si.20231106.16

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ACS Style

Wei-feng, C.; Lei, S. Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Sci. Innov. 2023, 11(6), 259-265. doi: 10.11648/j.si.20231106.16

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AMA Style

Wei-feng C, Lei S. Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Sci Innov. 2023;11(6):259-265. doi: 10.11648/j.si.20231106.16

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@article{10.11648/j.si.20231106.16,
  author = {Chen Wei-feng and Su Lei},
  title = {Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis},
  journal = {Science Innovation},
  volume = {11},
  number = {6},
  pages = {259-265},
  doi = {10.11648/j.si.20231106.16},
  url = {https://doi.org/10.11648/j.si.20231106.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20231106.16},
  abstract = {Traditional information dissemination channels can no longer meet the needs of tourists for in-depth cultural tourism experience. More and more tourists hope to make their own tourism plans through the feedback from other tourists, and management departments also need to make strategies for scenic spot optimization by referring to the feedback from scenic spot experience. This study uses word frequency statistics and sentiment analysis based on deep learning to evaluate the perception of aesthetic, cultural and service values of Sha Mian Island scenic spot, using image semantic cutting to perceive the tendency of architectural photography, discover the shortcomings of the scenic spot and give suggestions for optimisation. The results show that the aesthetic value and greenery level of Sha Mian Island is high, the scenic content is vague, and the image data is not ideal for the perception of human and service content. This study provides a way of research that has a wide range of data sources, is easy to operate and can be quickly calculated and analysed in a short period of time with time-sensitive evaluations and pictures, giving a way of research that provides optimisation solutions for tourist attractions.
},
 year = {2023}
}

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TY  - JOUR
T1  - Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis
AU  - Chen Wei-feng
AU  - Su Lei
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.si.20231106.16
DO  - 10.11648/j.si.20231106.16
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 259
EP  - 265
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20231106.16
AB  - Traditional information dissemination channels can no longer meet the needs of tourists for in-depth cultural tourism experience. More and more tourists hope to make their own tourism plans through the feedback from other tourists, and management departments also need to make strategies for scenic spot optimization by referring to the feedback from scenic spot experience. This study uses word frequency statistics and sentiment analysis based on deep learning to evaluate the perception of aesthetic, cultural and service values of Sha Mian Island scenic spot, using image semantic cutting to perceive the tendency of architectural photography, discover the shortcomings of the scenic spot and give suggestions for optimisation. The results show that the aesthetic value and greenery level of Sha Mian Island is high, the scenic content is vague, and the image data is not ideal for the perception of human and service content. This study provides a way of research that has a wide range of data sources, is easy to operate and can be quickly calculated and analysed in a short period of time with time-sensitive evaluations and pictures, giving a way of research that provides optimisation solutions for tourist attractions.

VL  - 11
IS  - 6
ER  -

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Author Information

Chen Wei-feng

College of Art and Design, Zhongshan Institute, University of Electronic Science and Technology, Zhongshan, China

Contact Email
Su Lei

College of Art and Design, Zhongshan Institute, University of Electronic Science and Technology, Zhongshan, China

Contact Email

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Plain Text BibTeX RIS

APA Style

Wei-feng, C., Lei, S. (2023). Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Science Innovation, 11(6), 259-265. https://doi.org/10.11648/j.si.20231106.16

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ACS Style

Wei-feng, C.; Lei, S. Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Sci. Innov. 2023, 11(6), 259-265. doi: 10.11648/j.si.20231106.16

Copy | Download

AMA Style

Wei-feng C, Lei S. Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis. Sci Innov. 2023;11(6):259-265. doi: 10.11648/j.si.20231106.16

Copy | Download

@article{10.11648/j.si.20231106.16,
  author = {Chen Wei-feng and Su Lei},
  title = {Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis},
  journal = {Science Innovation},
  volume = {11},
  number = {6},
  pages = {259-265},
  doi = {10.11648/j.si.20231106.16},
  url = {https://doi.org/10.11648/j.si.20231106.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20231106.16},
  abstract = {Traditional information dissemination channels can no longer meet the needs of tourists for in-depth cultural tourism experience. More and more tourists hope to make their own tourism plans through the feedback from other tourists, and management departments also need to make strategies for scenic spot optimization by referring to the feedback from scenic spot experience. This study uses word frequency statistics and sentiment analysis based on deep learning to evaluate the perception of aesthetic, cultural and service values of Sha Mian Island scenic spot, using image semantic cutting to perceive the tendency of architectural photography, discover the shortcomings of the scenic spot and give suggestions for optimisation. The results show that the aesthetic value and greenery level of Sha Mian Island is high, the scenic content is vague, and the image data is not ideal for the perception of human and service content. This study provides a way of research that has a wide range of data sources, is easy to operate and can be quickly calculated and analysed in a short period of time with time-sensitive evaluations and pictures, giving a way of research that provides optimisation solutions for tourist attractions.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Research on Perception Analysis and Optimization of Tourist Attractions Based on Image Semantic Analysis
AU  - Chen Wei-feng
AU  - Su Lei
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.si.20231106.16
DO  - 10.11648/j.si.20231106.16
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 259
EP  - 265
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20231106.16
AB  - Traditional information dissemination channels can no longer meet the needs of tourists for in-depth cultural tourism experience. More and more tourists hope to make their own tourism plans through the feedback from other tourists, and management departments also need to make strategies for scenic spot optimization by referring to the feedback from scenic spot experience. This study uses word frequency statistics and sentiment analysis based on deep learning to evaluate the perception of aesthetic, cultural and service values of Sha Mian Island scenic spot, using image semantic cutting to perceive the tendency of architectural photography, discover the shortcomings of the scenic spot and give suggestions for optimisation. The results show that the aesthetic value and greenery level of Sha Mian Island is high, the scenic content is vague, and the image data is not ideal for the perception of human and service content. This study provides a way of research that has a wide range of data sources, is easy to operate and can be quickly calculated and analysed in a short period of time with time-sensitive evaluations and pictures, giving a way of research that provides optimisation solutions for tourist attractions.

VL  - 11
IS  - 6
ER  -

Copy | Download