A Semantic and Sentiment Analysis on Online Neighborhood Reviews for Understanding the Perceptions of People toward Their Living Environments

The perceptions of people toward neighborhoods reveal their satisfaction with their living environments and their perceived quality of life. Recently, there is an emergence of Web sites designed for helping people to find suitable places to live. On these Web sites, 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 article, we analyze such online neighborhood review data. Specifically, we extract two types of knowledge from the data: (1) semantics, or the semantic topics (or aspects) that people talk about regarding their neighborhoods, and (2) sentiments, or 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 data set of online reviews about the neighborhoods in New York City, which were contributed by 7,673 distinct Web users. We also conduct correlation analyses between the subjective perceptions extracted from this data set and the objective socioeconomic attributes of New York City 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.