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Author: jmsquare

topic_modeling.R

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+
+########################################
+#          TOPIC MODELLING             #
+########################################
+install.packages("topicmodels")
+install.packages("SnowballC")
+library(SnowballC)
+library(topicmodels)
+
+path <- "C:/Users/JM/Documents/project"
+quest <- read.csv("C:/Users/JM/Documents/questionnaire.csv",sep=";")
+
+q1 <- quest["question1"]
+question <- q1[!is.na(q1[,1]),1]
+my.stopwords <- c()
+
+###############################
+#Generation of the DocumentTerm Matrix
+###############################
+
+DTM <- DocumentTermMatrix(generateCorpus(question,my.stopwords),control=list(tokenize=NGramTokenizer,stemming = function(x) wordStem(x, language = "french")))
+rowTotals <- apply(DTM , 1, sum)
+DTM.new   <- DTM[rowTotals> 0, ]    
+
+################
+#Latent Dirichlet allocation
+###########################
+
+lda <- LDA(DTM.new,7)
+terms(lda,8)
+TermDocumentMatrix()
+
+######################
+#Display using wordcloud
+##########################
+wordcloud.generate=function(corpus,min.freq=3,minWordLength=3,dictionnaire){
+  doc.m = TermDocumentMatrix(corpus, control = list(minWordLength = 3,tokenize=NGramTokenizer))
+  dm = as.matrix(doc.m)
+  # calculate the frequency of words
+  v = sort(rowSums(dm), decreasing=TRUE)
+  d = data.frame(word=names(v), freq=v)
+  d <- d[d$word %in% dict,]
+  # Create palette
+  pal <- brewer.pal(11, "Dark2")
+  #Generate the wordcloud
+  wc=wordcloud(d$word, 
+               d$freq, 
+               min.freq=5, 
+               scale = c(2, 0.5), 
+               colors = pal,
+               max.words = 100,
+               rot.per = 0.2,
+               random.order = FALSE)
+  #Display the wordcloud
+  wc
+}