Koning Filip lijkt op …

March 26,2019
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(This article was first published on bnosac :: open analytical helpers - bnosac :: open analytical helpers,and kindly contributed to 188bet appR-bloggers)

Last call for thecourse on Text Mining with R,held next week in Leuven,Belgium on April 1-2.Viewing the course description as well as subscription can be done athttps://lstat.kuleuven.be/training/coursedescriptions/text-mining-with-r

Some things you'll learn … is that King Filip of Belgium is similar to public expenses if we just look at open data from questions and answers in Belgian parliament (retrieved from herehttp://data.dekamer.be).Proof is below.See you next week.koning filip

library(ruimtehol)
library(data.table)
library(lattice)
library(latticeExtra)
data("dekamer",package = "ruimtehol")
dekamer$x <- strsplit(dekamer$question,"\\W")
dekamer$x <- lapply(dekamer$x,FUN = function(x) setdiff(x,""))
dekamer$x <- sapply(dekamer$x,FUN = function(x) paste(x,collapse = " "))
dekamer$x <- tolower(dekamer$x)
dekamer$y <- strsplit(dekamer$question_theme,split = ",")
dekamer$y <- lapply(dekamer$y,FUN=function(x) gsub(" ","-",x))
set.seed(321)
model <- embed_tagspace(x = dekamer$x,y = dekamer$y,
early_stopping = 0.8,validationPatience = 10,
dim = 50,
lr = 0.01,epoch = 40,loss = "softmax",adagrad = TRUE,
similarity = "cosine",negSearchLimit = 50,
ngrams = 2,minCount = 2)embedding_words  <- as.matrix(model,type = "words")
embedding_labels <- as.matrix(model,type = "labels",prefix = FALSE)
embedding_person <- starspace_embedding(model,tolower(c("Theo Francken")))
embedding_person <- starspace_embedding(model,tolower(c("Koning Filip")))
similarities <- embedding_similarity(embedding_person,embedding_words,top = 9)
similarities <- subset(similarities,!term2 %in% c("koning","filip"))
similarities$term <- factor(similarities$term2,levels = rev(similarities$term2))
plt1 <- barchart(term ~ similarity | term1,data = similarities,
scales = list(x = list(relation = "free"),y = list(relation = "free")),
col = "darkgreen",xlab = "Similarity",main = "Koning Filip lijkt op ...")similarities <- embedding_similarity(embedding_person,embedding_labels,top = 7)
similarities$term <- factor(similarities$term2,levels = rev(similarities$term2))
plt2 <- barchart(term ~ similarity | term1,data = similarities,
scales = list(x = list(relation = "free"),y = list(relation = "free")),
col = "darkgreen",xlab = "Similarity",main = "Koning Filip lijkt op ...")
c(plt1,plt2)

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