Enhancing museum visitor forecasting using deep learning and sentiment analysis: A transformer-based approach for sustainable management

PLOS ONE's publication of a study on enhancing museum visitor forecasting using deep learning and sentiment analysis marks a significant development in the application of AI for predictive modeling. This study's focus on integrating structured and unstructured data to forecast museum visitor numbers has implications for website owners, particularly those in the cultural and tourism sectors. By leveraging similar techniques, website owners can better analyze user sentiment and behavior, ultimately informing their content strategies and improving user experience. For instance, a museum's website could utilize sentiment analysis to gauge visitor reactions to exhibitions, allowing for more targeted marketing and improved engagement. To capitalize on these insights, website owners can take several actionable steps: firstly, explore integrating AI-powered sentiment analysis tools into their website analytics to gain a deeper understanding of user preferences; secondly, consider implementing a transformer-based approach to forecasting website traffic, allowing for more accurate predictions and informed decision-making; and thirdly, review their llms.txt files to ensure they are properly managing AI bot traffic, which can be crucial in maintaining accurate analytics and preventing skewed forecasting models.