Assessing the perceptions of urban parks and understanding the relationships between environmental features and park perceptions are critical to the design and management of urban parks. However, it is challenging to quantify park perceptions at a large spatial-temporal scale. In addition, little is known about which environmental features contribute to different perceptions in cross-cultural comparison. This study collected 21462 and 28398 online reviews from 2015 to 2020 and applied Natural Language Processing (NLP) methods to understand how people perceive urban parks and which environmental features contribute to positive and negative park perceptions in Shanghai and Brussels. We quantified park perceptions through sentiment analysis and transformed unstructured high-frequency words into structured environmental feature clusters corresponding to positive and negative perceptions using the word2vec models and machine learning. In addition, we created Chinese-based and English-based environmental feature lexicons for standardized measurement of the perceived frequencies of different environmental features and their associations with park perceptions. The results of our sentiment analysis showed that urban parks were generally perceived positively in both cities. We also identified 15 environmental feature clusters that were related to positive and negative perceptions. Beta regression results showed that the perceived frequencies of water bodies and colorfulness could be used as positive predictors for park perceptions in Shanghai. In contrast, the perceived frequency of tranquility had a positive impact on park perceptions in Brussels. We found that the perceived frequency of crowdedness was also positively associated with park perceptions in Brussels, suggesting the potential use conflicts between different user groups in urban parks. This study can provide insights into park design and management from cross-cultural comparison through systematic identification and standardized assessment of landscape preferences using online reviews and NLP methods.

评估城市公园的感知并理解环境特征与公园感知之间的关系对于城市公园的设计和管理至关重要。然而，在大的时空尺度上量化公园感知是具有挑战性的。此外，对于跨文化比较中哪些环境特征会导致不同的看法，我们知之甚少。本研究收集了 2015 年至 2020 年的 21462 条和 28398 条在线评论，并应用自然语言处理 (NLP) 方法来了解人们如何看待城市公园以及哪些环境特征会影响上海和布鲁塞尔的积极和消极公园认知。我们通过情感分析量化公园感知，并使用 word2vec 模型和机器学习将非结构化高频词转换为与正面和负面感知相对应的结构化环境特征集群。此外，我们创建了基于中文和基于英语的环境特征词典，用于标准化测量不同环境特征的感知频率及其与公园感知的关联。我们的情绪分析结果表明，两个城市普遍对城市公园的看法是积极的。我们还确定了 15 个与正面和负面看法相关的环境特征集群。Beta回归结果表明，水体感知频率和色彩色彩可作为上海公园感知的正向预测因子。相比之下，人们感知到的宁静频率对布鲁塞尔的公园感知产生了积极影响。我们发现，拥挤的感知频率也与布鲁塞尔的公园感知呈正相关，表明城市公园中不同用户群体之间存在潜在的使用冲突。本研究可以通过使用在线评论和 NLP 方法对景观偏好进行系统识别和标准化评估，从跨文化比较中提供对公园设计和管理的见解。我们发现拥挤的感知频率也与布鲁塞尔的公园感知呈正相关，表明城市公园不同用户群体之间存在潜在的使用冲突。本研究可以通过使用在线评论和 NLP 方法对景观偏好进行系统识别和标准化评估，从跨文化比较中提供对公园设计和管理的见解。我们发现拥挤的感知频率也与布鲁塞尔的公园感知呈正相关，表明城市公园不同用户群体之间存在潜在的使用冲突。本研究可以通过使用在线评论和 NLP 方法对景观偏好进行系统识别和标准化评估，从跨文化比较中提供对公园设计和管理的见解。