TY - JOUR
KW - Classification (of information)
KW - Cultural heritages
KW - Data Analysis
KW - Digital museums
KW - Digital reconstruction
KW - museums
KW - Sentiment Analysis
KW - Sentiment analysis
KW - Support vector
KW - Support vector machines
KW - Support vectors machine
KW - Technology
KW - Unbalanced data
KW - Unbalanced support
KW - User evaluations
KW - User experience evaluations
KW - Users  experiences
KW - Vectors
KW - Virtual reality
KW - Virtual reality
KW - article
KW - data analysis
KW - human
KW - human experiment
KW - information center
KW - Inheritance
KW - reference value
KW - Satisfaction
KW - sentiment analysis
KW - support vector machine
AU - Xiang Chen
AU - Zhiwei Chen
AU - Lei Xiao
AU - Ming Zhou
AB - With the development of virtual reality and digital reconstruction technology, digital museums have been widely promoted in various cities. Digital museums offer new ways to display and disseminate cultural heritage. It allows remote users to autonomously browse displays in a physical museum environment in a digital space. It is also possible to reproduce the lost heritage through digital reconstruction and restoration, so as to digitally present tangible cultural heritage and intangible cultural heritage to the public. However, the user s experience of using digital museums has not been fully and deeply studied at present. In this study, the user s experience evaluation data of digital museum are classified and processed, so as to analyze the user s emotional trend towards the museum. Considering that the user s evaluation data are unbalanced data, this study uses an unbalanced support vector machine (USVM) in the classification of user evaluation data. The main idea of this method is that the boundary of the support vector is continuously shifted to the majority class by repeatedly oversampling some support vectors until the real support vector samples are found. The experimental results show that the classification obtained by the used USVM has a good practical reference value. Based on the classification results of the evaluation data, the construction of the digital museum can be further guided and maintained, thereby improving the user experience satisfaction of the museum. This research will make an important contribution to the construction of the museum and the inheritance of culture.
BT - Computational Intelligence And Neuroscience
DA - apr
DO - 10.1155/2022/2096634
N2 - With the development of virtual reality and digital reconstruction technology, digital museums have been widely promoted in various cities. Digital museums offer new ways to display and disseminate cultural heritage. It allows remote users to autonomously browse displays in a physical museum environment in a digital space. It is also possible to reproduce the lost heritage through digital reconstruction and restoration, so as to digitally present tangible cultural heritage and intangible cultural heritage to the public. However, the user s experience of using digital museums has not been fully and deeply studied at present. In this study, the user s experience evaluation data of digital museum are classified and processed, so as to analyze the user s emotional trend towards the museum. Considering that the user s evaluation data are unbalanced data, this study uses an unbalanced support vector machine (USVM) in the classification of user evaluation data. The main idea of this method is that the boundary of the support vector is continuously shifted to the majority class by repeatedly oversampling some support vectors until the real support vector samples are found. The experimental results show that the classification obtained by the used USVM has a good practical reference value. Based on the classification results of the evaluation data, the construction of the digital museum can be further guided and maintained, thereby improving the user experience satisfaction of the museum. This research will make an important contribution to the construction of the museum and the inheritance of culture.
PY - 2022
T2 - Computational Intelligence And Neuroscience
TI - A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis Technology
VL - 2022
SN - 1687-5265
ER -