R Deep Learning Projects

From words to vectors

We are ready to create the word embedding using GloVe. First, let's initialize an instance of the GlobalVectors class:

glove <- GlobalVectors$new(word_vectors_size = 50,                            vocabulary = vocab,                            x_max = 10)

We now apply the fit_transform method (scikit learn users might be familiar with it):

wv_main <- glove$fit_transform(tcm,                                 n_iter = 10,                                 convergence_tol = 0.01)

And once this is done, we have our vectorizer ready. We now need to parse our text: 

text <- unlist(imdb$review)length(text)# 25000text_df <- data_frame(line = 1:25000, text = text)

And apply the unnest_tokens functions from tidytext to turn our data in a tidy format:

text_df <- text_df %>% unnest_tokens(word, text)head(text_df)

This gives a familiar output: ...