Freakonometrics

Un nouveau billet pour reprendre une analyse marrante faite par @3wen  (Ewen Gallic, qui travaille à Montréal alors que je profite de la Suisse). Suite à l'analyse amusante des trolls de Twitter, j'avais lancé l'idée qu'il pourrait être amusant de regarder parmi les députés français (que j'avais un peu suivis l'autre jour), qui tweete avec qui. Ewen a eu la bonne idée de regarder sur  http://lelab.europe1.fr/ ce qui lui a permis de récupérer la liste des comptes Twitter des députés, en France. L'idée est simple: parmi les députés français, on regarde qui suit qui. Quelqu'un de très suivi sera au centre du nuage, alors que quelqu'un qui se contente de suivre sera sur le bord (intensément connecté au reste du nuage). Pour les personnes peu familières, Twitter n'est pas Facebook: on n'a pas des "amis", il y a des gens que l'on suit parce qu'ils racontent des choses qui peuvent nous intéresser.

En utilisant gephi Ewen a ensuite pu visualiser le graphique des interconnexions entre les députés.

Sans grande surprise, les députés de gauche suivent essentiellement les députés de gauche, et inversement. Quelques gros comptes font la passerelle entre les deux groupes parlementaires.

En fait, si on regarde en détail (voire sur le fichier image complet), on peut observer un peu mieux ce qui se passe,

Bon, la grande difficulté est de lire ces interactions correctement. En particulier, on ne peut pas conclure (à la vue seule du graphique) que Cécile Duflot est de gauche ! Ce que cela nous dit est que ce qu'elle raconte intéresse les gens de gauche (ou en tous cas les députés du Parti Socialiste), et pas du tout les gens de droite (les députés de l'UMP) ! On notera aussi que le centre n'existe pas. Sur Twitter en tous cas. Et si on regarde tout en bas, à droite, on retrouve le Front National, et on a la confirmation que ce que raconte le Front National n'intéresse personne !

We are currently experiencing an extremely hot month in Montréal (and more generally in North America). Looking at people having a beer, and starting the first barbecue of the year, I was wondering: if we asked people if global warming was a good or a bad thing, what do you think they will answer ? Wearing a T-shirt in Montréal in March is nice, trust me ! So how can we study, from a quantitative point of view, depending on the time of the year, what people think of global warming ?

A few month ago, I went quickly through Jeffrey Bree's tutorial (online on his blog) on sentiment analysis, where he was studying perception of airline companies in the US based on Twitter messages. His idea was to define a score function, counting the number of positive and negative words in a short sentence (Twitter is then perfect to run such a study) mentioning an Airline company. Consider the following score function (the one proposed by Jeffrey)

score.sentiment = function(sentences, pos.words,
neg.words, .progress='none')
{
require(plyr)
require(stringr)
scores = laply(sentences, 
function(sentence, pos.words, neg.words) {
sentence = gsub('[[:punct:]]', '', sentence)
sentence = gsub('[[:cntrl:]]', '', sentence)
sentence = gsub('\\d+', '', sentence)
sentence = tolower(sentence)
word.list = strsplit(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)
}

A list of positive and negative words (in English) can be found on http://www.cs.uic.edu/ (see e.g. the chapter on sentiment analysis).

hu.liu.pos = scan("positive-words.txt", what="character",
comment.char=';')
hu.liu.neg = scan('negative-words.txt', what='character',
comment.char=';')

The score here is simple the difference between positive versus negative words. For instance, the following sentence has a score of +3,

> score.sentiment("It's awesome I am so happy,
thank you all",
+ hu.liu.pos,hu.liu.neg)$score
[1] 3

while the following is -3.

> score.sentiment("I'm desperate, life is a nightmare,
I want to die",
+ hu.liu.pos,hu.liu.neg)$score
[1] -3

But one can easy see a big problem with this methodology. What if the sentence included negations ? E.g.

> score.sentiment("I'm no longer desperate, life is
not a nightmare anymore I don't want to die",
+ hu.liu.pos,hu.liu.neg)$score
[1] -3

Here the sentence is negative, extremely negative, if we look only at the score. But it should be the opposite. I simple idea is to change (slightly) the function, so that once a negation is found in the sentence, we take the opposite of the score. Hence, we just add at the end of the function

if("not"%in%words){score=-score}

Here we obtain

> score.sentiment.neg("I'm no longer desperate,
life is not a nightmare anymore I don't want to die",
+ hu.liu.pos,hu.liu.neg)$score
[1] 3

But does it really work ? Let us focus on Tweets,

library(twitteR)

Consider the following tweet-extractions, based on two words, a negative word, and the negation of a positive word,

> tweets=searchTwitter('not happy',n=1000)
> NH.text= lapply(tweets, function(t) t$getText() )
> NH.scores = score.sentiment(NH.text,
+ hu.liu.pos,hu.liu.neg)
 
> tweets=searchTwitter('unhappy',n=1000)
> UH.text= lapply(tweets, function(t) t$getText() )
> UH.scores = score.sentiment(UH.text,
+ hu.liu.pos,hu.liu.neg)

If we draw score density in tweets, we see that scores are quite different,

> plot(density(NH.scores$score,bw=.8),col="red")
> lines(density(UH.scores$score,bw=.8),col="blue")

If now we use the second function, taking the opposite of the score when the sentence contains a negation, we obtain

> UH.scores = score.sentiment.neg(UH.text,
+ hu.liu.pos,hu.liu.neg)
> NH.scores = score.sentiment.neg(NH.text,
+ hu.liu.pos,hu.liu.neg)
> plot(density(NH.scores$score,bw=.8),col="red")
> lines(density(UH.scores$score,bw=.8),col="blue")

So we can admit that our modified function is not that bad.

Let us now focus on tweets in North American. More specifically, we focus on tweets sent within the following region (in blue on the right), in order to avoid problems of hemispheres. Then, we restrict ourselves to specific days, or periods of time. For instance, we can wonder if snow is associated with positive or negative words, and if we enjoy snow when it does arrive, by the end of November, and if we start to find it boring by the end of March ?

Considerer the following research

> w.tweets=searchTwitter("snow",since= LISTEDATE[k],
+ until= LISTEDATE[k+1],geocode="40,-100,2000mi")
> W.text= lapply(w.tweets, function(t) t$getText() )
> W.scores = score.sentiment.neg(W.text,
+ hu.liu.pos,hu.liu.neg, .progress='text')
> M[k]=mean(W.scores$score)

> w.tweets=searchTwitter("global warming",since= LISTEDATE[k],
+ until= LISTEDATE[k+1],geocode="40,-100,2000mi")

What we simply observe is that global warming was related to "negative words" on Twitter a few years ago, but we have reached a neutral position nowadays.