r - Using SVM and mapreduce for text classification -

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i'm new hadoop , mapreduce , trying use mapreduce train model using svm classify messages categories.

currently, can on small set of data without mapreduce , rtexttools , e1701 use mapreduce train model larger training data.

below code have used on small dataset.

library("e1071") library("rtexttools")  predicttest <- function(test_text, mat, classifier){ train_mat = mat[1:2,] train_mat[,1:ncol(train_mat)] = 0  test_matrix = create_matrix(test_text, language="english",removestopwords=t, removenumbers=t,stemwords=t, tolower=t, removepunctuation=t) test_mat <- as.matrix(test_matrix)  for(col in colnames(test_mat)){ if(col %in% colnames(train_mat)) {   train_mat[2,col] = test_mat[1,col]; } }  #test_mat = as.matrix(t(test_mat)) row.names(train_mat)[1] = "" row.names(train_mat)[2] = test_text p <- predict(classifier, train_mat[1:2,]) as.character(p[2]) }  #using smaller set of training data (large memory requirements) training_data1 <- read.csv("train_final_combined.csv", header=t) matrix <- create_matrix(training_data1$text, language="english", removestopwords=t, removenumbers=t,stemwords=t, tolower=t, removepunctuation=t, removesparseterms=0.998) mat = as.matrix(matrix)  svm_classifier = svm(mat, as.factor(training_data1$category) ) predicttest("emma stone bringing style", mat, svm_classifier)

sample of training data: https://i.stack.imgur.com/m9pep.png


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