A Study on Public Cognition of Industrial Heritage Based on Emotion Analysis Algorithm

Abstract

Industrial heritage sites depict crucial connections to the industrial past, and public perception and appreciation of these sites vary widely. The visitors emotionally engage with industrial heritage and can inform techniques to improve cultural preservation and tourism experiences.Conventional approaches are lacking in analyzing the public's sentiment toward industrial heritage. To overcome this problem, we proposed an Advanced Northern Goshawk-Responsive Generative Adversarial Network (ANG-RGAN) method. This approach recognizes the public cognition of implementing an emotion in industrial heritage. ANG-RGAN utilizedacombination structure that combines attention mechanisms and an RGAN structure to improve sentiment recognition precision and provide a reliable evaluation of emotions in a variety of industrial heritage scenarios. First, we collect the data from surveys and interviews conducted with visitors to various industrial heritage sites in Beijing. An emotion analysis algorithm processed textual data from these surveys to identify and categorize emotional expressions such as fascination, intrigue, sadness, happiness, and nostalgia.The proposed method involves preprocessing textual data from visitor reviews using tokenization and normalization strategies and extracting emotional features using term frequency-inverse document frequency (TF–IDF) techniques. The experimental result of the proposed method isanalysed in terms of f1-score (98.3%), precision (98.2%), accuracy (98.9%), and recall (98.5%). It demonstrates that the proposed ANG-RGAN method achieves the greatest performance of information security in industrial heritage based on emotion analysis when compared to existing approaches. The proposed ANG-RGAN approach improves the evaluation of emotions to comprehend how the general public perceives industrial heritage, offering perceptions that are vital for cultural preservation plans and the growth of the tourism industry.