Innovative Applications of Deep Learning in Cultural Heritage Development and Preservation: A Customization Perspective

Cultural heritage sites play a vital role in preserving a nation's history, traditions, and identity. However, to enhance the visitor experience and cater to diverse preferences, it is crucial to understand the sentiments and opinions expressed in site reviews. Existing sentiment analysis approaches have primarily focused on product reviews or social media data, failing to capture the unique context and nuances of cultural heritage site reviews. Current work on analyzing reviews of cultural sites only focuses on identifying positive, negative, or neutral sentiments. None of them worked towards customized analysis based on different user parameters such as age group, location, gender, purpose of visit, or group size. Thus, there is a lack of systems that provide tailored insights and enhance visitor experiences for diverse demographic groups visiting cultural heritage sites. To address this challenge we propose a deep learning-based system to analyse and strategize planning of maintaining and developing cultural sites Our approach leverages deep neural networks to perform sentiment analysis based on visitor reviews, age group, location, gender, purpose of visit, or group size, etc. This customized sentiment analysis enables cultural heritage site operators to gain insights into visitor experiences, identify areas for improvement, and tailor their offerings to meet the preferences of diverse crowds. The applied LSTM model offers powerful capabilities for sentiment analysis in the context of enhancing visitor experiences at heritage sites. By leveraging demographic data in conjunction with sentiment analysis, these models can provide valuable insights and enable personalized, data-driven approaches to improving visitor satisfaction and engagementTherefore, the proposed work may prove useful in strategize planning, maintaining, and developing of cultural sites in a low cost across the globe.