A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis...

Area of Science:

• Digital Heritage
• Human-Computer Interaction
• Cultural Informatics

Background:

• Digital museums leverage virtual reality and digital reconstruction for cultural heritage dissemination.
• Current research lacks in-depth analysis of user experience within digital museum environments.
• Digital platforms offer novel ways to access and virtually restore tangible and intangible cultural heritage.

Purpose of the Study:

• To analyze user emotional trends in digital museums by classifying user evaluation data.
• To address the challenge of unbalanced datasets in user experience evaluation.
• To provide a data-driven approach for improving digital museum construction and user satisfaction.

Main Methods:

• Classification and processing of user experience evaluation data from digital museums.
• Application of an unbalanced support vector machine (USVM) for handling imbalanced datasets.
• USVM methodology involves oversampling support vectors to identify true samples and improve classification accuracy.

Main Results:

• The unbalanced support vector machine (USVM) demonstrated effective classification of user evaluation data.
• Classified data provides practical reference value for digital museum development.
• The study successfully analyzed user emotional trends, identifying key areas for improvement.

Conclusions:

• The classification results derived from USVM can guide the ongoing construction and maintenance of digital museums.
• Improving digital museum design based on user feedback enhances overall user satisfaction.
• This research contributes significantly to digital museum development and cultural heritage preservation.