Study on Satisfaction Evaluation of Ecotourism Through Advanced Network Text Mining - Jia Gao; Leong Mow Gooi; Kim Mee Chong; Xiang Lyu; Jin Zhang (IEEE Access)
This study investigates ecotourism satisfaction using advanced text mining and optimization techniques. The primary objective was to enhance the analysis of tourist feedback by leveraging Long Short-Term Memory (LSTM) networks and Genetic Algorithms (GA). Text mining was conducted using LSTM to capture and analyze sequential patterns in unstructured feedback data, revealing insights into visitor satisfaction levels over different phases of their trips. The LSTM model was further optimized using GA to improve its performance by fine-tuning hyperparameters such as learning rate and network depth. Key findings indicate that the GA-optimized LSTM model achieved a 15% increase in accuracy, a 20% improvement in precision, and a 25% boost in recall compared to the unoptimized model. The F1 score also rose by 18%, demonstrating the effectiveness of the optimization process. These improvements underscore the potential of integrating deep learning with optimization techniques to refine predictive models in the context of ecotourism. The significance of this study lies in its ability to provide a more nuanced understanding of tourist satisfaction through sophisticated analytical methods. By identifying key themes and sentiment patterns from visitor feedback, the study offers actionable insights that can inform better management practices and enhance visitor experiences. This approach not only advances the field of ecotourism satisfaction analysis but also sets a precedent for future research involving complex data-driven methodologies in tourism and hospitality management.