Abstract
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.
Abstract (translated)
情感分析方法正在迅速成为城市设计领域的热门选择，以便对城市环境进行 crowdsourced 评估。然而，该领域中使用的大多数模型仅能识别文本评价中的积极或消极情感，而无法推断其中包含的具体城市方面的情感或它们所带来的情感。虽然情感基于 aspects 的情感分析（ASSA）变得越来越受欢迎，但大多数现有的 ABSA 模型都是基于非城市主题进行训练的，如餐厅、电子产品、消费品等。这项研究开发了一个能够提取位于地理定位的文本城市评估中的城市方面以及相应方面情感分类的 ABSA 模型。我们在波士顿公共公园的 2500 条 crowdsourced 评论的数据集上进行标注，并使用 Local Context Focus (LCF) 的双向编码器表示从 Transformers (BERT) 模型中进行训练。我们的模型在 Aspect 词提取（ATE）和 Aspect 情感分类（ASC）任务上取得了显著的提高。为了演示分析，我们在波士顿的各个城市方面之间进行了空间可视化。我们希望这个模型对于设计师和规划师进行精细化的城市情感评估有所帮助。
URL
https://arxiv.org/abs/2312.12253
