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Promoting positive emotional experiences for tourists is crucial for sustaining development in rural areas. However, existing research has limited focus on the rural built environment, particularly in developing a framework to evaluate environmental sentiment on a small to medium scale with detailed indicators. This study addresses this gap by examining the impact of the rural built environment on tourists' emotions. Natural Language Processing (NLP) technologies are employed to analyze web text data and determine the average sentiment index for traditional villages in Fuzhou, China. Additionally, data on the built environment were acquired through the HRnet segmentation model and Matlab. To assess the association between environmental indicators and the sentiment index, we used eXtreme Gradient Boosting (XGBoost), the SHapley Additive exPlanation (SHAP) model, and ArcMap software. The study demonstrated that (1) the spatial distribution of the average sentiment index was significant. Houfu Village (9.91), Qianhu Village (9.88), and Ximen Village (9.75) had the highest scores, while Doukui Village (−0.85), Jiji Village (0.2), and Qiaodong Village (0.55) had the lowest. (2) The indicators that have the most significant impact on sentiment are Openness, Greenness, and Color Complexity, with a contribution value above 0.7—followed by Enclosure, Visual Entropy, and Ground Exposure, with a contribution between 0.5 and 0.7. Furthermore, analyzing the interaction mechanism of the indicators showed a non-linear relationship. The environmental characteristics associated with high emotional index scores are openness in the range of 0.2 to 0.5, greenness in the range of 0.4 to 0.6, and color complexity in the range of 0.3 to 0.5. This study provides observations pertinent to the sustainable development of traditional village environments. The findings contribute to an understanding of how these environmental elements might be effectively designed to improve tourists' sentiment in rural settings.
Chen et al. (Thu,) studied this question.