Predict Sentiment of the Text

[rakashi]

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

[haoopeng]

I actually didn't write code to predict new data samples. What I did in train_keras_embedding.ipynb was training the model and, after each epoch, evaluating it on the validation data on the fly.

If you want to use the trained model for further prediction, please first save the model (can be in Python pickle format), and load it in memory each time for prediction.

The implementation is as simple as one line of code calling the model API. Please refer to Keras online documentation (see the predict section): https://keras.io/models/model/ Thanks.