Mining the last French presidential debate

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After reading this post (thanks to him), I think it could be interesting to replicate this with some specific up of french language and to see and we can perform rapid view of the debate between Sarkozy and Hollande of the last 2nd round of presidential election.

Key words : TextMining, Elections, France, Debate, 2nd Round

We use the packages qdap from (Tyler Rinker) and tm to perform textmining analysis and the classical package like ggplot or RColorBrewer make our  graphics look pretty.
For Hollande


# 18/05/2013
# Key words : TextMining, Elections, France, Debate, 2nd Round
# We use the packages qdap from (donner le lien) and
# tm to perform textmining analysis and the classical
# package like ggplot or RColorBrewer to get the graphics pretty.
suppressPackageStartupMessages(require(twitteR))
suppressPackageStartupMessages(require(XML))
suppressPackageStartupMessages(require(tm))
suppressPackageStartupMessages(require(rgdal))
suppressPackageStartupMessages(require(ggplot2))
suppressPackageStartupMessages(require(qdap))
suppressPackageStartupMessages(require(rJava))
suppressPackageStartupMessages(library(wordcloud))
library(Rstem)
setwd("D:/PERSO/R_Working/Tutoriels/TextMining")
# Hollande
debate <- read.transcript("./Data/debat2tours.docx", col.names=c("person", "dialogue"))
htruncdf(debate,5,50)
# We keep just Holland's word
Hollande = subset(debate,person=="HOLLANDE")
# We define the stop words
sw=c("a","ou",tm::stopwords("fr"),"c'est", "n'est","s'y","qu'on","s'il","ah",
letters,"ca","n'y","d'un","monsieur")
generateCorpus= function(df,my.stopwords=c()){
text2.corpus= Corpus(VectorSource(df),readerControl=list(language="fr"))
text2.corpus = tm_map(text2.corpus, removePunctuation)
text2.corpus = tm_map(text2.corpus, tolower)
text2.corpus= tm_map(text2.corpus, removeNumbers)
text2.corpus = tm_map(text2.corpus, removeWords, stopwords("fr"))
text2.corpus = tm_map(text2.corpus, removeWords, my.stopwords)
#text2.corpus <- tm_map(text2.corpus, stemDocument, language = "french")
}
HollandeCorpus<-generateCorpus(Hollande,sw)
# We build a Term Document Matrix
H.tdm <- TermDocumentMatrix(HollandeCorpus)
H.m <- as.matrix(H.tdm)
H.v <- sort(rowSums(H.m),decreasing=TRUE)
H.d <- data.frame(word = names(H.v),freq=H.v)
H.d = subset(H.d,freq<=90)
H.d = subset(H.d,freq>=3)
H.d$stem <- wordStem(row.names(H.d), language = "french")
# and put words to column, otherwise they would be lost when aggregating
H.d$word <- row.names(H.d)
agg_freq <- stats::aggregate(freq ~ stem, data = H.d, sum)
agg_word <- stats::aggregate(word ~ stem, data = H.d, function(x) x[1])
forW <- cbind(freq = agg_freq[, 2], agg_word)
# sort by frequency
forW <- forW[order(forW$freq, decreasing = T), ]
# Wordcloud
col<- brewer.pal(8,"Dark2")
png("wordcloud_Hollande.png", width=1280,height=800)
wordcloud(forW$word,forW$freq, scale=c(8,.2),min.freq=5,
max.words=Inf, random.order=FALSE, rot.per=.20, colors=col)
dev.off()




Top words From hollande
For Sarkozy
Top words from Sarkozy
As we can see, I’ve a problem to manage french accent. If somebody have any idea… We can also perform a quick Gantt plot basing on Qdap package and get some information about who lead the debate
debate2 = subset(debate,person=="SARKOZY"|person == "HOLLANDE")
debate2$person<- factor(debate2$person,levels=qcv(terms="SARKOZY HOLLANDE"))
png("Gant.png", width=700,height=500)
with(debate2, gantt_plot(dialogue, person, xlab = "duration(words)", scale = "free"))
dev.off()
view raw Miningdebate2.r hosted with ❤ by GitHub
No surprise about the winner.

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