sentiment_analysis.py

# https://deeplearningcourses.com/c/deep-learning-advanced-nlp
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future


import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from keras.models import Model
from keras.layers import Dense, Embedding, Input
from keras.layers import LSTM, GRU
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences

import keras.backend as K
if len(K.tensorflow_backend._get_available_gpus()) > 0:
  from keras.layers import CuDNNLSTM as LSTM
  from keras.layers import CuDNNGRU as GRU



# some configuration
MAX_SEQUENCE_LENGTH = 50
MAX_VOCAB_SIZE = 20000
EMBEDDING_DIM = 10
VALIDATION_SPLIT = 0.2
BATCH_SIZE = 128
EPOCHS = 5



# get the data at: https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews
# prepare text samples and their labels
print('Loading in data...')
train = pd.read_csv("../large_files/kaggle-sentiment-analysis/train.tsv", sep='\t')
sentences = train["Phrase"].values
targets = (train['Sentiment'].values > 3)
K = len(set(targets))




# convert the sentences (strings) into integers
tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)

print("max sequence length:", max(len(s) for s in sequences))
print("min sequence length:", min(len(s) for s in sequences))
s = sorted(len(s) for s in sequences)
print("median sequence length:", s[len(s) // 2])

maxlen = min(max(len(s) for s in sequences), MAX_SEQUENCE_LENGTH)



# get word -> integer mapping
word2idx = tokenizer.word_index
print('Found %s unique tokens.' % len(word2idx))


# pad sequences so that we get a N x T matrix
data = pad_sequences(sequences, maxlen=maxlen)
print('Shape of data tensor:', data.shape)


print('Building model...')

# create an LSTM network with a single LSTM
input_ = Input(shape=(maxlen,))
x = Embedding(len(word2idx) + 1, EMBEDDING_DIM)(input_)
x = LSTM(5)(x)
output = Dense(K, activation='softmax')(x)

model = Model(input_, output)
model.compile(
  loss='sparse_categorical_crossentropy',
  optimizer='adam',
  metrics=['accuracy']
)


print('Training model...')
r = model.fit(
  data,
  targets,
  batch_size=BATCH_SIZE,
  epochs=EPOCHS,
  validation_split=VALIDATION_SPLIT
)

# plot some data
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()
plt.show()

# accuracies
plt.plot(r.history['acc'], label='acc')
plt.plot(r.history['val_acc'], label='val_acc')
plt.legend()
plt.show()