# Koning Filip lijkt op …

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Last call for the **course 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 at https://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 here http://data.dekamer.be). Proof is below. See you next week.

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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