Sentiment analysis methods are rapidly being adopted by the field of Urban Design and Planning, for the crowdsourced evaluation of urban environments. However, most models used within this domain are able to identify positive or negative sentiment associated with a textual appraisal as a whole, without inferring information about specific urban aspects contained within it, or the sentiment associated with them. While Aspect Based Sentiment Analysis (ABSA) is becoming increasingly popular, most existing ABSA models are trained on non-urban themes such as restaurants, electronics, consumer goods and the like. This body of research develops an ABSA model capable of extracting urban aspects contained within geo-located textual urban appraisals, along with corresponding aspect sentiment classification. We annotate a dataset of 2500 crowdsourced reviews of public parks, and train a Bidirectional Encoder Representations from Transformers (BERT) model with Local Context Focus (LCF) on this data. Our model achieves significant improvement in prediction accuracy on urban reviews, for both Aspect Term Extraction (ATE) and Aspect Sentiment Classification (ASC) tasks. For demonstrative analysis, positive and negative urban aspects across Boston are spatially visualized. We hope that this model is useful for designers and planners for fine-grained urban sentiment evaluation.

翻译：情感分析方法正被城市设计与规划领域迅速采纳，用于城市环境的众包评价。然而，该领域使用的大多数模型仅能识别文本评价整体的正面或负面情感，而无法推断其中包含的具体城市要素信息及其相关情感。尽管方面级情感分析日益普及，但现有ABSA模型大多针对非城市主题（如餐厅、电子产品、消费品等）进行训练。本研究开发了一种ABSA模型，能够从带有地理位置的文本城市评价中提取城市要素，并实现相应的方面情感分类。我们标注了一个包含2500条公园众包评价的数据集，并在该数据上训练了具备局部上下文聚焦的BERT模型。我们的模型在城市评价的方面项提取和方面情感分类任务上均显著提升了预测准确性。为进行示例分析，我们对波士顿各区域的城市正面和负面要素进行了空间可视化。希望该模型能为设计师和规划者提供精细化的城市情感评价支持。