haoopeng/cnn-yelp-challenge-2016-sentiment-classification

IPython Notebook for training a word-level Convolutional Neural Network model for sentiment classification task on Yelp-Challenge-2016 review dataset.

cnn-yelp-challenge-2016-sentiment-classification's Issues

About version of the dataset

Which round of the yelp challenge dataset are you using in this repository?

ValueError: too many values to unpack

Hi Sir,

I am running the code to train multi class and im getting the error too many values to unpack using the yelp dataset.

(reviews_words_index, labels) = pickle.load(open("review_sents_1859888.pkl", 'rb'))
ValueError: too many values to unpack.

Predict Sentiment of the Text

Hi
I have trained subset of Yelp data using keras 1.0.0 I got 399850by50reviews_words_index.pkl, review_sents_1859888.pkl how can I use these files for predicting my own text and Here my code is shown below

'''
train cnn mode for sentiment classification on yelp data set
author: hao peng
'''
import pickle
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from Word2VecUtility import Word2VecUtility
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.embeddings import Embedding
from keras.layers.convolutional import Convolution1D, MaxPooling1D

def get_volcabulary_and_list_words(data):
reviews_words = []
volcabulary = []
for review in data["text"]:
review_words = Word2VecUtility.review_to_wordlist(
review, remove_stopwords=True)
reviews_words.append(review_words)
for word in review_words:
volcabulary.append(word)
volcabulary = set(volcabulary)
return volcabulary, reviews_words

def get_reviews_word_index(reviews_words, volcabulary, max_words, max_length):
word2index = {word: i for i, word in enumerate(volcabulary)}
# use w in volcabulary to limit index within max_words
reviews_words_index = [[start] + [(word2index[w] + index_from) for w in review] for review in reviews_words]
# in word2vec embedding, use (i < max_words + index_from) because we need the exact index for each word, in order to map it to its vector. And then its max_words is 5003 instead of 5000.
reviews_words_index = [[i if (i < max_words) else oov for i in index] for index in reviews_words_index]
# padding with 0, each review has max_length now.
reviews_words_index = sequence.pad_sequences(reviews_words_index, maxlen=max_length, padding='post', truncating='post')
return reviews_words_index

def vectorize_labels(labels, nums):
labels = np.asarray(labels, dtype='int32')
length = len(labels)
Y = np.zeros((length, nums))
for i in range(length):
Y[i, (labels[i]-1)] = 1.
return Y

data processing para

max_words = 5000
max_length = 50

model training parameters

batch_size = 32
embedding_dims = 100
nb_filter = 250
filter_length = 3
hidden_dims = 250
nb_epoch = 2

index trick parameters

index_from = 3
start = 1

padding = 0

oov = 2

data = pd.read_csv(
'review_sub_399850.tsv', header=0, delimiter="\t", quoting=3, encoding='utf-8')
print('get volcabulary...')
volcabulary, reviews_words = get_volcabulary_and_list_words(data)
print('get reviews_words_index...')
reviews_words_index = get_reviews_word_index(reviews_words, volcabulary, max_words, max_length)

print reviews_words_index[:20, :12]
print reviews_words_index.shape

labels = data["stars"]

pickle.dump((reviews_words_index, labels), open("399850by50reviews_words_index.pkl", 'wb'))

(reviews_words_index, labels) = pickle.load(open("399850by50reviews_words_index.pkl", 'rb'))

index = np.arange(reviews_words_index.shape[0])
train_index, valid_index = train_test_split(
index, train_size=0.8, random_state=100)

labels = vectorize_labels(labels, 5)
train_data = reviews_words_index[train_index]
valid_data = reviews_words_index[valid_index]
train_labels = labels[train_index]
valid_labels = labels[valid_index]
print train_data.shape
print valid_data.shape
print train_labels[:10]

del(labels, train_index, valid_index)

print "start training model..."

model = Sequential()

we start off with an efficient embedding layer which maps

our vocab indices into embedding_dims dimensions

model.add(Embedding(max_words + index_from, embedding_dims,
input_length=max_length))
model.add(Dropout(0.25))

we add a Convolution1D, which will learn nb_filter

word group filters of size filter_length:

filter_length is like filter size, subsample_length is like step in 2D CNN.

model.add(Convolution1D(nb_filter=nb_filter,
filter_length=filter_length,
border_mode='valid',
activation='relu',
subsample_length=1))

we use standard max pooling (halving the output of the previous layer):

model.add(MaxPooling1D(pool_length=2))

We flatten the output of the conv layer,

so that we can add a vanilla dense layer:

model.add(Flatten())

We add a vanilla hidden layer:

model.add(Dense(hidden_dims))
model.add(Dropout(0.25))
model.add(Activation('relu'))

We project onto 5 unit output layer, and activate it with softmax:

model.add(Dense(5))
model.add(Activation('softmax'))
print 'A',train_data.shape
print 'B',valid_data.shape
print train_data
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
class_mode='categorical')
model.fit(train_data, train_labels, batch_size=batch_size,
nb_epoch=nb_epoch, show_accuracy=True,
validation_data=(valid_data, valid_labels))

sentiment = model.predict(valid_data[1:])
print 'sentiment',sentiment

Predict

model.predict_classes(np.array(list("Hi How are you")), verbose=1)
print('prediction of [1, 1]: ', model.predict_classes(np.array([[1, 1]]), verbose=1))
print('prediction of [8, 8]: ', model.predict_classes(np.array([[8, 8]]), verbose=1))

I have tried with following method
model.predict but I am getting error, can any one help me