Sentiment in New York City: A High Resolution Spatial and Temporal View

TL;DR

This study uses Twitter data to create a detailed, real-time map of public sentiment in New York City, revealing spatial and temporal patterns of mood across different city areas and times.

Contribution

It introduces a Twitter-specific sentiment classifier and demonstrates high-resolution spatial and temporal sentiment analysis in NYC.

Findings

Public sentiment is highest in parks and lowest at transportation hubs.

Sentiment improves closer to Times Square.

Sentiment fluctuates daily and weekly, peaking at midnight and on weekends.

Abstract

Measuring public sentiment is a key task for researchers and policymakers alike. The explosion of available social media data allows for a more time-sensitive and geographically specific analysis than ever before. In this paper we analyze data from the micro-blogging site Twitter and generate a sentiment map of New York City. We develop a classifier specifically tuned for 140-character Twitter messages, or tweets, using key words, phrases and emoticons to determine the mood of each tweet. This method, combined with geotagging provided by users, enables us to gauge public sentiment on extremely fine-grained spatial and temporal scales. We find that public mood is generally highest in public parks and lowest at transportation hubs, and locate other areas of strong sentiment such as cemeteries, medical centers, a jail, and a sewage facility. Sentiment progressively improves with proximity to Times Square. Periodic patterns of sentiment fluctuate on both a daily and a weekly scale: more positive tweets are posted on weekends than on weekdays, with a daily peak in sentiment around midnight and a nadir between 9:00 a.m. and noon.