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

<p>Under the background of “cultural and tourism integration”, scenic cultural and creative experience products are an important means to enhance the communication power of scenic spots and generate income. In this paper, the CORE model is used as the design concept, and the CORE design process for scenic cultural and creative products is constructed. The plain Bayesian classification algorithm is used to extract review feedback from the product, and the vectorization of the extracted reviews is achieved using the FastText model. The vectorized reviews are input into the logistic regression classification model to obtain sentiment classification results. A fused sentiment feature vector is constructed by combining Apirior and a sentiment dictionary aimed at separating sentiment vector classification. Finally, the experience effect of cultural and creative products is explored as an example of X scenic spot, and the optimization strategy of product design is explored. The attribute feature of X Scenic Spot’s cultural and creative experience product, “color” embraces four emotional attributes, and the corresponding adaptations are 4.77, 5.42, 5.40, and 5.38, respectively. The SDs of the emotional attributes of the existing product are The SDs of the emotional attributes of the existing products are 1.141, 1.025, and 0.184, respectively, and the differences between the product descriptions and the users’ opinions on the perceptual attributes are large, which provides the evaluation correlation in the CORE model. Under the guidance of the CORE model, four aspects of cultural and creative marketing, scenic area IP, consumer needs and product experience dimensions are proposed.</p>
</abstract>

Under the background of “cultural and tourism integration”, scenic cultural and creative experience products are an important means to enhance the communication power of scenic spots and generate income. In this paper, the CORE model is used as the design concept, and the CORE design process for scenic cultural and creative products is constructed. The plain Bayesian classification algorithm is used to extract review feedback from the product, and the vectorization of the extracted reviews is achieved using the FastText model. The vectorized reviews are input into the logistic regression classification model to obtain sentiment classification results. A fused sentiment feature vector is constructed by combining Apirior and a sentiment dictionary aimed at separating sentiment vector classification. Finally, the experience effect of cultural and creative products is explored as an example of X scenic spot, and the optimization strategy of product design is explored. The attribute feature of X Scenic Spot’s cultural and creative experience product, “color” embraces four emotional attributes, and the corresponding adaptations are 4.77, 5.42, 5.40, and 5.38, respectively. The SDs of the emotional attributes of the existing product are The SDs of the emotional attributes of the existing products are 1.141, 1.025, and 0.184, respectively, and the differences between the product descriptions and the users’ opinions on the perceptual attributes are large, which provides the evaluation correlation in the CORE model. Under the guidance of the CORE model, four aspects of cultural and creative marketing, scenic area IP, consumer needs and product experience dimensions are proposed.

Design of cultural and creative experience products in scenic spots based on CORE model

Applied Mathematics and Nonlinear Sciences

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

Under the background of “cultural and tourism integration”, scenic cultural and creative experience products are an important means to enhance the...

Design of cultural and creative experience products in scenic spots based on CORE model

Online veröffentlicht: 18. Dez. 2023

Seitenbereich: -

Eingereicht: 17. Feb. 2023

Akzeptiert: 28. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01543

Schlüsselwörter
Plain Bayes, FastText model, Logistic regression, Sentiment vector, CORE model

© 2023 Ying Dai et al., published by Sciendo

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

Design of cultural and creative experience products in scenic spots based on CORE model

Online veröffentlicht: 18. Dez. 2023

Seitenbereich: -

Eingereicht: 17. Feb. 2023

Akzeptiert: 28. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01543

SchlüsselwörterPlain Bayes, FastText model, Logistic regression, Sentiment vector, CORE model

© 2023 Ying Dai et al., published by Sciendo

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

Schlüsselwörter
Plain Bayes, FastText model, Logistic regression, Sentiment vector, CORE model