Text Classification – Classifying product titles using Convolutional Neural Network and Word2Vec embedding

Text classification help us to better understand and organize data. I’ve tried building a simple CNN classifier using Keras with tensorflow as backend to classify products available on eCommerce sites. Data for this experiment are product titles of three distinct categories from a popular eCommerce site. Reference: Tutorial

tl;dr

Python notebook and data

 Collecting Data

For this experiment I’ve collected product titles belonging to the following categories.

  • Women’s clothing
  • Cameras
  • Home appliances

Since these categories are distinct, meaning they don’t have any overlap of contextual information, Our model should have less classification errors/perform well. I’ve tried to implement 2 proven architecture of CNN with Word2Vec embedding.

Setup

We need the following libraries

  • Gensim
  • Keras
  • NLTK
  • Pandas
  • Numpy
  • Tensorflow

and

  • Conda to manage virtual environment
  • Pre-trained vectors trained on Google News dataset download 1.5GB for Word2Vec embedding.
import numpy as np
import pandas as pd
from gensim.models import KeyedVectors
from keras.layers import Flatten
from keras.layers import MaxPooling1D
from keras.models import Model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
from nltk.corpus import stopwords
MAX_NB_WORDS = 200000
MAX_SEQUENCE_LENGTH = 30
EMBEDDING_DIM = 300
EMBEDDING_FILE = "../lib/GoogleNews-vectors-negative300.bin"
category_index = {"clothing":0, "camera":1, "home-appliances":2}
category_reverse_index = dict((y,x) for (x,y) in category_index.items())
STOPWORDS = set(stopwords.words("english"))

Loading Data

Download data. It is important to make sure that the data doesn’t have any null/Nan values.

clothing = pd.read_csv("clothing.tsv", sep='\t')
cameras = pd.read_csv("cameras.tsv", sep='\t')
home_appliances = pd.read_csv("home.tsv", sep='\t')
datasets = [clothing, cameras, home_appliances]
print("Make sure there are no null values in the datasets")
for data in datasets:
print("Has null values: ", data.isnull().values.any())
Make sure there are no null values in the datasets
Has null values:  False
Has null values:  False
Has null values:  False

Preprocessing

Stop words or words that occur frequently and is distracting are removed first, Then we use classes provided by Keras to help prepare text so it can be used by neural network models.

def preprocess(text):
text= text.strip().lower().split()
text = filter(lambda word: word not in STOPWORDS, text)
return " ".join(text)
for dataset in datasets:
dataset['title'] = dataset['title'].apply(preprocess)

To prepare the vector (array of integers) representation of text :

  • Combine titles from all three cateories to obtain a list of text.
  • Drop duplicates
  • Initialize tokenizer with num_words = MAX_NB_WORDS (200K). i.e. The tokenizer will perform a word count, sorted by number of occurences in descending order and pick top N words, 200K in this case
  • Use tokenizer’s texts_to_sequences method to convert text to array of integers.
  • The arrays obtained from previous step might not be of uniform length, use pad_sequences method to obtain arrays with length equal to MAX_SEQUENCE_LENGTH (30)
all_texts = clothing['title'] + cameras['title'] + home_appliances['title']
all_texts = all_texts.drop_duplicates(keep=False)
tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(all_texts)
clothing_sequences = tokenizer.texts_to_sequences(clothing['title'])
electronics_sequences = tokenizer.texts_to_sequences(cameras['title'])
home_appliances_sequences = tokenizer.texts_to_sequences(home_appliances['title'])
clothing_data = pad_sequences(clothing_sequences, maxlen=MAX_SEQUENCE_LENGTH)
electronics_data = pad_sequences(electronics_sequences, maxlen=MAX_SEQUENCE_LENGTH)
home_appliances_data = pad_sequences(home_appliances_sequences, maxlen=MAX_SEQUENCE_LENGTH)

word_index has a unique integer ID assigned to each word in the data. For example

word_index = tokenizer.word_index
test_string = "sports action spy pen camera"
print("word\t\tid")
print("-" * 20)
for word in test_string.split():
print("%s\t\t%s" % (word, word_index[word]))
word		id
--------------------
sports		16
action		13
spy		7
pen		55
camera		2

The tokenizer will replace words with unique integer id to get a vector representation of the title. Example:

test_sequence = tokenizer.texts_to_sequences(["sports action camera", "spy pen camera"])
padded_sequence = pad_sequences(test_sequence, maxlen=MAX_SEQUENCE_LENGTH)
print("Text to Vector", test_sequence)
print("Padded Vector", padded_sequence)
Text to Vector [[16, 13, 2], [7, 55, 2]]
Padded Vector [[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0  0  0 16 13  2]
 [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0  0  0  7 55  2]]

Product titles belonging to all three categories are kept separate so far for the sake of understanding. To prepare the input layer, All three cateogries are combined together and shuffled as shown below.

The category (y-axis or label) is converted to convnet’s understandable format (one hot vector) by using the keras.util method to_categorical. Example:

print("clothing: \t\t", to_categorical(category_index["clothing"], 3))
print("camera: \t\t", to_categorical(category_index["camera"], 3))
print("home appliances: \t", to_categorical(category_index["home-appliances"], 3))
clothing: 		 [[ 1.  0.  0.]]
camera: 		 [[ 0.  1.  0.]]
home appliances: 	 [[ 0.  0.  1.]]
print("clothing shape: ", clothing_data.shape)
print("electronics shape: ", electronics_data.shape)
print("home appliances shape: ", home_appliances_data.shape)
data = np.vstack((clothing_data, electronics_data, home_appliances_data))
category = pd.concat([clothing['category'], cameras['category'], home_appliances['category']]).values
category = to_categorical(category)
print("-"*10)
print("combined data shape: ", data.shape)
print("combined category/label shape: ", category.shape)
clothing shape:  (392721, 30)
electronics shape:  (1347, 30)
home appliances shape:  (11425, 30)
----------
combined data shape:  (405493, 30)
combined category/label shape:  (405493, 3)

Shuffling and splitting the data since categories are stacked one after the other. nb_validation_samples is the index which separates training and testing/validating sets. This step can be simplified by train_test_split from scikit.

VALIDATION_SPLIT = 0.4
indices = np.arange(data.shape[0]) # get sequence of row index
np.random.shuffle(indices) # shuffle the row indexes
data = data[indices] # shuffle data/product-titles/x-axis
category = category[indices] # shuffle labels/category/y-axis
nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])
x_train = data[:-nb_validation_samples]
y_train = category[:-nb_validation_samples]
x_val = data[-nb_validation_samples:]
y_val = category[-nb_validation_samples:]

word2vec embedding

Word2Vec brings in semantic similarity info which can be leveraged by the convnets. This experiment uses pre-trained vectors from Google news.One other option is GloVe.

word2vec = KeyedVectors.load_word2vec_format(EMBEDDING_FILE, binary=True)
print('Found %s word vectors of word2vec' % len(word2vec.vocab))
Found 3000000 word vectors of word2vec

The following examples should help understand the intent behind using a pre trained word2vec.

print("Odd word out:", word2vec.doesnt_match("banana apple grapes carrot".split()))
print("-"*10)
print("Cosine similarity between TV and HBO:", word2vec.similarity("tv", "hbo"))
print("-"*10)
print("Most similar words to Computers:", ", ".join(map(lambda x: x[0], word2vec.most_similar("computers"))))
print("-"*10)
Odd word out: carrot
----------
Cosine similarity between TV and HBO: 0.613064891522
----------
Most similar words to Computers: computer, laptops, PCs, laptop_computers, desktop_computers, Computers, laptop, notebook_computers, Dell_OptiPlex_desktop, automated_seismographs
----------

Keras embedding layer can be obtained by Gensim Word2Vec’s word2vec.get_keras_embedding(train_embeddings=False) method or constructed like shown below. The null word embeddings indicate the number of words not found in our pre-trained vectors (In this case Google News). This could possibly be unique words for brands in this context.

from keras.layers import Embedding
word_index = tokenizer.word_index
nb_words = min(MAX_NB_WORDS, len(word_index))+1
embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))
for word, i in word_index.items():
if word in word2vec.vocab:
embedding_matrix[i] = word2vec.word_vec(word)
print('Null word embeddings: %d' % np.sum(np.sum(embedding_matrix, axis=1) == 0))
embedding_layer = Embedding(embedding_matrix.shape[0], # or len(word_index) + 1
embedding_matrix.shape[1], # or EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)
Null word embeddings: 1473

Model

I recommend this (30 Min) video about how Convnets work to understand the layers. Below is the replication of 2 proven architectures. More can be found here

from keras.models import Sequential
from keras.layers import Conv1D, GlobalMaxPooling1D, Flatten
from keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation
model = Sequential()
model.add(embedding_layer)
model.add(Dropout(0.2))
model.add(Conv1D(300, 3, padding='valid',activation='relu',strides=2))
model.add(Conv1D(150, 3, padding='valid',activation='relu',strides=2))
model.add(Conv1D(75, 3, padding='valid',activation='relu',strides=2))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(150,activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(3,activation='sigmoid'))
model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['acc'])
model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_2 (Embedding)      (None, 30, 300)           817200    
_________________________________________________________________
dropout_9 (Dropout)          (None, 30, 300)           0         
_________________________________________________________________
conv1d_9 (Conv1D)            (None, 14, 300)           270300    
_________________________________________________________________
conv1d_10 (Conv1D)           (None, 6, 150)            135150    
_________________________________________________________________
conv1d_11 (Conv1D)           (None, 2, 75)             33825     
_________________________________________________________________
flatten_3 (Flatten)          (None, 150)               0         
_________________________________________________________________
dropout_10 (Dropout)         (None, 150)               0         
_________________________________________________________________
dense_9 (Dense)              (None, 150)               22650     
_________________________________________________________________
dropout_11 (Dropout)         (None, 150)               0         
_________________________________________________________________
dense_10 (Dense)             (None, 3)                 453       
=================================================================
Total params: 1,279,578
Trainable params: 462,378
Non-trainable params: 817,200
_________________________________________________________________
model_1 = Sequential()
model_1.add(embedding_layer)
model_1.add(Conv1D(250,3,padding='valid',activation='relu',strides=1))
model_1.add(GlobalMaxPooling1D())
model_1.add(Dense(250))
model_1.add(Dropout(0.2))
model_1.add(Activation('relu'))
model_1.add(Dense(3))
model_1.add(Activation('sigmoid'))
model_1.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['acc'])
model_1.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_2 (Embedding)      (None, 30, 300)           817200    
_________________________________________________________________
conv1d_12 (Conv1D)           (None, 28, 250)           225250    
_________________________________________________________________
global_max_pooling1d_3 (Glob (None, 250)               0         
_________________________________________________________________
dense_11 (Dense)             (None, 250)               62750     
_________________________________________________________________
dropout_12 (Dropout)         (None, 250)               0         
_________________________________________________________________
activation_5 (Activation)    (None, 250)               0         
_________________________________________________________________
dense_12 (Dense)             (None, 3)                 753       
_________________________________________________________________
activation_6 (Activation)    (None, 3)                 0         
=================================================================
Total params: 1,105,953
Trainable params: 288,753
Non-trainable params: 817,200
_________________________________________________________________
model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=2, batch_size=128)
score = model.evaluate(x_val, y_val, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Train on 243296 samples, validate on 162197 samples
Epoch 1/5
243296/243296 [==============================] - 22s 92us/step - loss: 0.1106 - acc: 0.9768 - val_loss: 0.1090 - val_acc: 0.9773
Epoch 2/5
243296/243296 [==============================] - 24s 97us/step - loss: 0.1102 - acc: 0.9770 - val_loss: 0.1091 - val_acc: 0.9775
Epoch 3/5
243296/243296 [==============================] - 21s 86us/step - loss: 0.1102 - acc: 0.9770 - val_loss: 0.1080 - val_acc: 0.9774
Epoch 4/5
243296/243296 [==============================] - 23s 93us/step - loss: 0.1096 - acc: 0.9772 - val_loss: 0.1088 - val_acc: 0.9776
Epoch 5/5
243296/243296 [==============================] - 24s 98us/step - loss: 0.1098 - acc: 0.9773 - val_loss: 0.1097 - val_acc: 0.9773
Test loss: 0.10969909843
Test accuracy: 0.977305375562
model_1.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=2, batch_size=128)
score = model_1.evaluate(x_val, y_val, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Train on 243296 samples, validate on 162197 samples
Epoch 1/5
243296/243296 [==============================] - 13s 52us/step - loss: 8.3458e-04 - acc: 0.9999 - val_loss: 9.0927e-04 - val_acc: 0.9999
Epoch 2/5
243296/243296 [==============================] - 12s 48us/step - loss: 7.2089e-04 - acc: 0.9999 - val_loss: 0.0011 - val_acc: 0.9999
Epoch 3/5
243296/243296 [==============================] - 12s 49us/step - loss: 7.2221e-04 - acc: 1.0000 - val_loss: 0.0012 - val_acc: 0.9999
Epoch 4/5
243296/243296 [==============================] - 12s 51us/step - loss: 7.1913e-04 - acc: 0.9999 - val_loss: 0.0010 - val_acc: 0.9999
Epoch 5/5
243296/243296 [==============================] - 12s 49us/step - loss: 6.7104e-04 - acc: 1.0000 - val_loss: 0.0011 - val_acc: 0.9999
Test loss: 0.00113550592472
Test accuracy: 0.999895189184

model_1 is better than the other. Below is an example on how to use this model.

example_product = "Nikon Coolpix A10 Point and Shoot Camera (Black)"
example_product = preprocess(example_product)
example_sequence = tokenizer.texts_to_sequences([example_product])
example_padded_sequence = pad_sequences(example_sequence, maxlen=MAX_SEQUENCE_LENGTH)
print("-"*10)
print("Predicted category: ", category_reverse_index[model_1.predict_classes(example_padded_sequence, verbose=0)[0]])
print("-"*10)
probabilities = model_1.predict(example_padded_sequence, verbose=0)
probabilities = probabilities[0]
print("Clothing Probability: ",probabilities[category_index["clothing"]] )
print("Camera Probability: ",probabilities[category_index["camera"]] )
print("home appliances probability: ",probabilities[category_index["home-appliances"]] )
----------
Predicted category:  camera
----------
Clothing Probability:  5.12844e-21
Camera Probability:  0.505056
home appliances probability:  5.71945e-23

Conclusion

My observation is that with neural networks, the time taken for feature engineering is considerable reduced and researchers spend most of their time in deciding the architecture of the neural network layers. Word2Vec embedding greatly contributes to improving the accuracy of the model.

9 thoughts on “Text Classification – Classifying product titles using Convolutional Neural Network and Word2Vec embedding

  1. Hi Rajmak! Thanks for sharing your knowledge.

    You got excellent coding skills! Love in particular your script for ‘null word embeddings’! Awesome!

    A few comments: for Word2Vec, we can safely ignore ‘stop words’. The optimizer ‘adam’ does often a better job than ‘rmsprop’. Last but not least, the dataset in general is small-ish, in particualar for ‘electronics’ and ‘home appliances’. I love DL, but with a small dataset, a logistic regression might perform equally well.

    1. Hi Franco, Thanks for your interest in my blog. I greatly appreciate your kind words and thoughtful comments that helped me improve.

    1. Hi Dhruv, Thanks for your interest in my blog.

      The zero’th index of the input dimension (for Embedding class of Keras) is reserved for masking/padding/no-data. This is done to give room for unknown word,
      i.e. in case the sequence contains a word that is not in the word index (dictionary), this word will be the unknown index (or zero’th index)
      https://keras.io/layers/embeddings/

  2. Hi Rajesh Manikka,

    Very meaningful explanation.
    I have followed your steps but got “Test loss: nan” after executing below code:

    model_1.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=5, batch_size=128)
    score = model_1.evaluate(x_val, y_val, verbose=0)
    print(‘Test loss:’, score[0])
    print(‘Test accuracy:’, score[1])

    What I missed?
    Please Help.

    1. Hi Ghanshyam,
      Appreciate your feedback and thanks for your interest in my blog.
      nan or “Not a number” could also mean its infinity. Please check your data, Finding outliers, normalizing data could help.

      1. Hello Rajesh Manikka,
        Thanks for your response.
        I have taken the same code & data provide in your given example.

        For “model_1” -> first Epoch gives proper value.
        Train on 243296 samples, validate on 162197 samples
        Epoch 1/5
        243296/243296 [==============================] – 13s 52us/step – loss: 8.3458e-04 – acc: 0.9999 – val_loss: 9.0927e-04 – val_acc: 0.9999

        But Epoch 2/5 -> gives loss: nan and same for rest of all.

        Thanks. Please Guide.

  3. Hey Rajesh,

    great Blog.
    One question remains in the back of my mind. How does the Embedding Layer know which word is meant by the given index? So where is the connection between the tokenizer and the embedding layer?

Leave a Reply to Ghanshyam Dabhi Cancel reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.