15.2. Sentiment Analysis: Using Recurrent Neural Networks¶ Open the notebook in SageMaker Studio Lab
Like word similarity and analogy tasks, we can also apply pretrained word vectors to sentiment analysis. Since the IMDb review dataset in Section 15.1 is not very big, using text representations that were pretrained on large-scale corpora may reduce overfitting of the model. As a specific example illustrated in Fig. 15.2.1, we will represent each token using the pretrained GloVe model, and feed these token representations into a multilayer bidirectional RNN to obtain the text sequence representation, which will be transformed into sentiment analysis outputs (). For the same downstream application, we will consider a different architectural choice later.
Fig. 15.2.1 This section feeds pretrained GloVe to an RNN-based architecture for sentiment analysis.¶
from mxnet import gluon, init, np, npx
from mxnet.gluon import nn, rnn
from d2l import mxnet as d2l
npx.set_np()
batch_size = 64
train_iter, test_iter, vocab = d2l.load_data_imdb(batch_size)
Downloading ../data/aclImdb_v1.tar.gz from http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz...
import torch
from torch import nn
from d2l import torch as d2l
batch_size = 64
train_iter, test_iter, vocab = d2l.load_data_imdb(batch_size)
15.2.1. Representing Single Text with RNNs¶
In text classifications tasks, such as sentiment analysis, a
varying-length text sequence will be transformed into fixed-length
categories. In the following BiRNN class, while each token of a text
sequence gets its individual pretrained GloVe representation via the
embedding layer (self.embedding), the entire sequence is encoded by
a bidirectional RNN (self.encoder). More concretely, the hidden
states (at the last layer) of the bidirectional LSTM at both the initial
and final time steps are concatenated as the representation of the text
sequence. This single text representation is then transformed into
output categories by a fully-connected layer (self.decoder) with two
outputs (“positive” and “negative”).
class BiRNN(nn.Block):
def __init__(self, vocab_size, embed_size, num_hiddens,
num_layers, **kwargs):
super(BiRNN, self).__init__(**kwargs)
self.embedding = nn.Embedding(vocab_size, embed_size)
# Set `bidirectional` to True to get a bidirectional RNN
self.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers,
bidirectional=True, input_size=embed_size)
self.decoder = nn.Dense(2)
def forward(self, inputs):
# The shape of `inputs` is (batch size, no. of time steps). Because
# LSTM requires its input's first dimension to be the temporal
# dimension, the input is transposed before obtaining token
# representations. The output shape is (no. of time steps, batch size,
# word vector dimension)
embeddings = self.embedding(inputs.T)
# Returns hidden states of the last hidden layer at different time
# steps. The shape of `outputs` is (no. of time steps, batch size,
# 2 * no. of hidden units)
outputs = self.encoder(embeddings)
# Concatenate the hidden states at the initial and final time steps as
# the input of the fully-connected layer. Its shape is (batch size,
# 4 * no. of hidden units)
encoding = np.concatenate((outputs[0], outputs[-1]), axis=1)
outs = self.decoder(encoding)
return outs
class BiRNN(nn.Module):
def __init__(self, vocab_size, embed_size, num_hiddens,
num_layers, **kwargs):
super(BiRNN, self).__init__(**kwargs)
self.embedding = nn.Embedding(vocab_size, embed_size)
# Set `bidirectional` to True to get a bidirectional RNN
self.encoder = nn.LSTM(embed_size, num_hiddens, num_layers=num_layers,
bidirectional=True)
self.decoder = nn.Linear(4 * num_hiddens, 2)
def forward(self, inputs):
# The shape of `inputs` is (batch size, no. of time steps). Because
# LSTM requires its input's first dimension to be the temporal
# dimension, the input is transposed before obtaining token
# representations. The output shape is (no. of time steps, batch size,
# word vector dimension)
embeddings = self.embedding(inputs.T)
self.encoder.flatten_parameters()
# Returns hidden states of the last hidden layer at different time
# steps. The shape of `outputs` is (no. of time steps, batch size,
# 2 * no. of hidden units)
outputs, _ = self.encoder(embeddings)
# Concatenate the hidden states of the initial time step and final
# time step to use as the input of the fully connected layer. Its
# shape is (batch size, 4 * no. of hidden units)
encoding = torch.cat((outputs[0], outputs[-1]), dim=1)
# Concatenate the hidden states at the initial and final time steps as
# the input of the fully-connected layer. Its shape is (batch size,
# 4 * no. of hidden units)
outs = self.decoder(encoding)
return outs
Let us construct a bidirectional RNN with two hidden layers to represent single text for sentiment analysis.
embed_size, num_hiddens, num_layers, devices = 100, 100, 2, d2l.try_all_gpus()
net = BiRNN(len(vocab), embed_size, num_hiddens, num_layers)
net.initialize(init.Xavier(), ctx=devices)
embed_size, num_hiddens, num_layers, devices = 100, 100, 2, d2l.try_all_gpus()
net = BiRNN(len(vocab), embed_size, num_hiddens, num_layers)
def init_weights(m):
if type(m) == nn.Linear:
nn.init.xavier_uniform_(m.weight)
if type(m) == nn.LSTM:
for param in m._flat_weights_names:
if "weight" in param:
nn.init.xavier_uniform_(m._parameters[param])
net.apply(init_weights);
15.2.2. Loading Pretrained Word Vectors¶
Below we load the pretrained 100-dimensional (needs to be consistent
with embed_size) GloVe embeddings for tokens in the vocabulary.
glove_embedding = d2l.TokenEmbedding('glove.6b.100d')
glove_embedding = d2l.TokenEmbedding('glove.6b.100d')
Print the shape of the vectors for all the tokens in the vocabulary.
embeds = glove_embedding[vocab.idx_to_token]
embeds.shape
(49346, 100)
embeds = glove_embedding[vocab.idx_to_token]
embeds.shape
torch.Size([49346, 100])
We use these pretrained word vectors to represent tokens in the reviews and will not update these vectors during training.
net.embedding.weight.set_data(embeds)
net.embedding.collect_params().setattr('grad_req', 'null')
net.embedding.weight.data.copy_(embeds)
net.embedding.weight.requires_grad = False
15.2.3. Training and Evaluating the Model¶
Now we can train the bidirectional RNN for sentiment analysis.
lr, num_epochs = 0.01, 5
trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': lr})
loss = gluon.loss.SoftmaxCrossEntropyLoss()
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)
loss 0.293, train acc 0.880, test acc 0.850
662.1 examples/sec on [gpu(0), gpu(1)]
lr, num_epochs = 0.01, 5
trainer = torch.optim.Adam(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss(reduction="none")
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)
loss 0.282, train acc 0.880, test acc 0.850
819.4 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]
We define the following function to predict the sentiment of a text
sequence using the trained model net.
#@save
def predict_sentiment(net, vocab, sequence):
"""Predict the sentiment of a text sequence."""
sequence = np.array(vocab[sequence.split()], ctx=d2l.try_gpu())
label = np.argmax(net(sequence.reshape(1, -1)), axis=1)
return 'positive' if label == 1 else 'negative'
#@save
def predict_sentiment(net, vocab, sequence):
"""Predict the sentiment of a text sequence."""
sequence = torch.tensor(vocab[sequence.split()], device=d2l.try_gpu())
label = torch.argmax(net(sequence.reshape(1, -1)), dim=1)
return 'positive' if label == 1 else 'negative'
Finally, let us use the trained model to predict the sentiment for two simple sentences.
predict_sentiment(net, vocab, 'this movie is so great')
'positive'
predict_sentiment(net, vocab, 'this movie is so bad')
'negative'
predict_sentiment(net, vocab, 'this movie is so great')
'positive'
predict_sentiment(net, vocab, 'this movie is so bad')
'negative'
15.2.4. Summary¶
Pretrained word vectors can represent individual tokens in a text sequence.
Bidirectional RNNs can represent a text sequence, such as via the concatenation of its hidden states at the initial and final time steps. This single text representation can be transformed into categories using a fully-connected layer.
15.2.5. Exercises¶
Increase the number of epochs. Can you improve the training and testing accuracies? How about tuning other hyperparameters?
Use larger pretrained word vectors, such as 300-dimensional GloVe embeddings. Does it improve classification accuracy?
Can we improve the classification accuracy by using the spaCy tokenization? You need to install spaCy (
pip install spacy) and install the English package (python -m spacy download en). In the code, first, import spaCy (import spacy). Then, load the spaCy English package (spacy_en = spacy.load('en')). Finally, define the functiondef tokenizer(text): return [tok.text for tok in spacy_en.tokenizer(text)]and replace the originaltokenizerfunction. Note the different forms of phrase tokens in GloVe and spaCy. For example, the phrase token “new york” takes the form of “new-york” in GloVe and the form of “new york” after the spaCy tokenization.