train_lstm: train_lstm

In alex-gottlieb/deepIdeology: Scale Ideological Slant of Twitter Posts

Description

This function trains the LSTM model to identify the ideological slant of Tweets.

Usage

train_lstm(X_train, y_train, embeddings = "w2v", embedding_dim = 25,
  bidirectional = FALSE, convolutional = FALSE)

Arguments

X_train	data.frame or matrix of vectorized Tweets
y_train	Labels for training data. 0 for liberal, 1 for conservative.
embeddings	Type of word embedding algorithm to use. Options are "w2v" (word2vec), "glove", or "random" (random initialization).
embedding_dim	Length of word embeddings to use. Options are 25, 50, 100, or 200.
bidirectional	Optionally train on text sequences in reverse as well as forwards.
convolutional	Optionally apply convolutional filter to text sequences. Can only be used when bidirectional = TRUE

Note

Models are automatically saved in HDF5 format to a sub-folder of the root-directory called "models". File format is "{model type}_{embedding type}_{embedding dimensionality}d.h5".

Examples

# train a Bi-LSTM network using GloVe embeddings
data("ideo_tweets")
ideo_tokenizer <- text_tokenizer(num_words=20000)
ideo_tokenizer <- fit_text_tokenizer(ideo_tokenizer, ideo_tweets$text)
texts <- texts_to_vectors(ideo_tweets$text, ideo_tokenizer)
labels <- tweets$ideo_cat

train_test <- train_test_split(texts, labels)
X_train <- train_test$X_train
y_trian <- train_test$y_train
train_ltsm(X_train, ty_train, embeddings="glove", bidirectional=TRUE)

alex-gottlieb/deepIdeology documentation built on Nov. 1, 2019, 9:09 p.m.