Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments

TL;DR

This paper presents a geo-located aspect-based sentiment analysis model tailored for urban environments, enabling detailed evaluation of public perceptions of city spaces through crowdsourced reviews.

Contribution

It introduces a novel ABSA model trained on urban reviews, specifically designed to extract urban aspects and their sentiments from geo-located textual data.

Findings

Model improves accuracy in urban aspect extraction and sentiment classification

Annotated dataset of 2500 reviews of public parks used for training

Spatial visualization of urban sentiment in Boston demonstrates practical application

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.