The perceptions of people toward neighborhoods reveal their satisfactions with their living environments and their perceived quality of life. Recently, there is an emergence of websites designed for helping people to find suitable places to live. On these websites, current and previous residents can review their neighborhoods by providing numeric ratings and textual comments. Such online neighborhood review data provide novel opportunities for studying the perceptions of people toward their neighborhoods. In this work, we analyze such online neighborhood review data. Specifically, we extract two types of knowledge from the data: 1) semantics, i.e., the semantic topics (or aspects) that people talk about their neighborhoods; and 2) sentiments, i.e., the emotions that people express toward the different aspects of their neighborhoods. We experiment with a number of different computational models in extracting these two types of knowledge and compare their performances. The experiments are based on a dataset of online reviews about the neighborhoods in New York City (NYC), which were contributed by 7,673 distinct Web users. We also conduct correlation analyses between the subjective perceptions extracted from this dataset and the objective socioeconomic attributes of NYC neighborhoods, and find similarities and differences. The effective models identified in this research can be applied to neighborhood reviews in other cities for supporting urban planning and quality of life studies.
More details about this work can be found in our full paper: Yingjie Hu, Chengbin Deng, and Zhou Zhou (2019): A semantic and sentiment analysis on online neighborhood reviews for understanding the perceptions of people toward their living environment. Annals of the American Association of Geographers, 109(4), 1052-1073. [PDF]
(a) Some neighborhood reviews on Niche; (b) average ratings of NYC neighborhoods based on Niche review data.
Eight semantic topics discovered from the online reviews using LDA.
Average neighborhood perception maps for the eight semantic topics using LARA.
Media coverage about this work:
- Phys.org: How online neighborhood reviews could aid urban planning
- Smart Cities Dive: A new source of planning data: online reviews
- Planetizen: Study Examines the Benefits of Online Reviews to Planning Research
- IEEE GlobalSpec: Online reviews can help in future urban planning