Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced
  Evaluation of Urban Environments
Demircan Tas Rohit Priyadarshi Sanatani
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.