A natural language processing framework for collecting, analyzing, and visualizing users' sentiment on the built environment: case implementation of New York City and Seoul residences

Abstract

This study suggests a natural language processing framework for collecting, analyzing, and, visualizing online natural language data, consisting of a web crawler for data collection, tokenizer for text preprocessing, Word2vec for word embedding, and deep-learning long short-term memory networks for sentiment classification. The framework was exemplified on online brokerage platforms in New York City and Seoul. The visualized framework-driven results showed regional similarities and differences between the cities. The proposed approach provides a way to gather big data, not through surveys or interviews. The framework-driven analysis may provide descriptive precursors to explore how laypersons experience built environments and city spaces.