  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.
