How Do Cities Feel?

If you are lost and feel alone, circumnavigate the globe (For You, Coldplay)

You can not consider yourself a R-blogger until you do an analysis of Twitter using twitteR package. Everybody knows it. So here I go.

Inspired by the fabulous work of Jonathan Harris I decided to compare human emotions of people living (or twittering in this case) in different cities. My plan was analysing tweets generated in different locations of USA and UK with one thing in common: all of them must contain the string “I FEEL”. These are the main steps I followed:

• Locate cities I want to analyze using world cities database of maps package
• Download tweets around these locations using searchTwitter function of twitteR package.
• Cross tweets with positive and negative lists of words and calculate a simple scoring for each tweet as number of positive words – number of negative words
• Calculate how many tweets have non-zero scoring; since these tweets put into words some emotion I call them sentimental tweets
• Represent cities in a bubble chart where x-axis is percentage of sentimental tweets, y-axis is average scoring and size of bubble is population

This is the result of my experiment:

These are my conclusions (please, do not take it seriously):

• USA cities seem to have better vibrations and are more sentimental than UK ones
• Capital city is the happiest one for both countries
• San Francisco (USA) is the most sentimental city of the analysis; on the other hand, Liverpool (UK) is the coldest one
• The more sentimental, the better vibrations

From my point of view, this analysis has some important limitations:

• It strongly depends on particular events (i.e. local football team wins the championship)
• I have no idea of what kind of people is behind tweets
• According to my experience, searchTwitter only works well for a small number of searches (no more than 300); for larger number of tweets to return, it use to give malformed JSON response error from server

Anyway, I hope it will serve as starting point of some other analysis in the future. At least, I learned interesting things about R doing it.

Here you have the code:

library(twitteR)
library(RCurl)
library(maps)
library(plyr)
library(stringr)
library(bitops)
library(scales)
#Register
if (!file.exists('cacert.perm'))
{
  download.file(url = 'http://curl.haxx.se/ca/cacert.pem', destfile='cacert.perm')
}
requestURL="https://api.twitter.com/oauth/request_token"
accessURL="https://api.twitter.com/oauth/access_token"
authURL="https://api.twitter.com/oauth/authorize"
consumerKey = "YOUR CONSUMER KEY HERE"
consumerSecret = "YOUR CONSUMER SECRET HERE"
Cred <- OAuthFactory$new(consumerKey=consumerKey,
                         consumerSecret=consumerSecret,
                         requestURL=requestURL,
                         accessURL=accessURL,
                         authURL=authURL)
Cred$handshake(cainfo=system.file("CurlSSL", "cacert.pem", package="RCurl"))
#Save credentials
save(Cred, file="twitter authentification.Rdata")
load("twitter authentification.Rdata")
registerTwitterOAuth(Cred)
options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl")))
#Cities to analyze
cities=data.frame(
  CITY=c('Edinburgh', 'London', 'Glasgow', 'Birmingham', 'Liverpool', 'Manchester',
         'New York', 'Washington', 'Las Vegas', 'San Francisco', 'Chicago','Los Angeles'),
  COUNTRY=c("UK", "UK", "UK", "UK", "UK", "UK", "USA", "USA", "USA", "USA", "USA", "USA"))
data(world.cities)
cities2=world.cities[which(!is.na(match(
str_trim(paste(world.cities$name, world.cities$country.etc, sep=",")),
str_trim(paste(cities$CITY, cities$COUNTRY, sep=","))
))),]
cities2$SEARCH=paste(cities2$lat, cities2$long, "10mi", sep = ",")
cities2$CITY=cities2$name
#Download tweets
tweets=data.frame()
for (i in 1:nrow(cities2))
{
  tw=searchTwitter("I FEEL", n=400, geocode=cities2[i,]$SEARCH)
  tweets=rbind(merge(cities[i,], twListToDF(tw),all=TRUE), tweets)
}
#Save tweets
write.csv(tweets, file="tweets.csv", row.names=FALSE)
#Import csv file
city.tweets=read.csv("tweets.csv")
#Download lexicon from http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar
hu.liu.pos = scan('lexicon/positive-words.txt',  what='character', comment.char=';')
hu.liu.neg = scan('lexicon/negative-words.txt',  what='character', comment.char=';')
#Function to clean and score tweets
score.sentiment=function(sentences, pos.words, neg.words, .progress='none')
{
  require(plyr)
  require(stringr)
  scores=laply(sentences, function(sentence, pos.word, neg.words) {
    sentence=gsub('[[:punct:]]','',sentence)
    sentence=gsub('[[:cntrl:]]','',sentence)
    sentence=gsub('\\d+','',sentence)
    sentence=tolower(sentence)
    word.list=str_split(sentence, '\\s+')
    words=unlist(word.list)
    pos.matches=match(words, pos.words)
    neg.matches=match(words, neg.words)
    pos.matches=!is.na(pos.matches)
    neg.matches=!is.na(neg.matches)
    score=sum(pos.matches) - sum(neg.matches)
    return(score)
  }, pos.words, neg.words, .progress=.progress)
  scores.df=data.frame(score=scores, text=sentences)
  return(scores.df)
}
cities.scores=score.sentiment(city.tweets[1:nrow(city.tweets),], hu.liu.pos, hu.liu.neg, .progress='text')
cities.scores$pos2=apply(cities.scores, 1, function(x) regexpr(",",x[2])[1]-1)
cities.scores$CITY=apply(cities.scores, 1, function(x) substr(x[2], 1, x[3]))
cities.scores=merge(x=cities.scores, y=cities, by='CITY')
df1=aggregate(cities.scores["score"], by=cities.scores[c language="("CITY")"][/c], FUN=length)
names(df1)=c("CITY", "TWEETS")
cities.scores2=cities.scores[abs(cities.scores$score)>0,]
df2=aggregate(cities.scores2["score"], by=cities.scores2[c language="("CITY")"][/c], FUN=length)
names(df2)=c("CITY", "TWEETS.SENT")
df3=aggregate(cities.scores2["score"], by=cities.scores2[c language="("CITY")"][/c], FUN=mean)
names(df3)=c("CITY", "TWEETS.SENT.SCORING")
#Data frame with results
df.result=join_all(list(df1,df2,df3,cities2), by = 'CITY', type='full')
#Plot results
radius <- sqrt(df.result$pop/pi)
symbols(100*df.result$TWEETS.SENT/df.result$TWEETS, df.result$TWEETS.SENT.SCORING, circles=radius,
        inches=0.85, fg="white", bg="gold", xlab="Sentimental Tweets", ylab="Scoring Of Sentimental Tweets (Average)",
        main="How Do Cities Feel?")
text(100*df.result$TWEETS.SENT/df.result$TWEETS, df.result$TWEETS.SENT.SCORING, paste(df.result$CITY, df.result$country.etc, sep="-"), cex=1, col="gray50")