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Chapter 5
You might also notice that a text is not really a matrix but more a vector because two
words located in adjacent rows of text have very little in common. Indeed, this is a
major difference when compared with images, where two pixels located in adjacent
columns are likely to have some degree of correlation.
Now you might wonder: I understand that you represent the text as a vector but,
in doing so, we lose the position of the words. This position should be important,
shouldn't it? Well, it turns out that in many real applications, knowing that whether
a sentence contains a particular basic unit (a char, a word, an aggregate) or not is
pretty useful information even if we don't keep track of where exactly in the sentence
this basic unit is located.
For instance, ConvNets achieve pretty good results for "Sentiment Analysis" where
we need to understand if a piece of text has a positive or a negative sentiment;
for "Spam Detection" where we need to understand if a piece of text is useful
information or spam; and for "Topic Categorization", where we need to understand
what a piece of text is all about. However, ConvNets are not well suited for a Partof-Speech
(POS) analysis, where the goal is to understand what the logical role of
every single word is (for example, a verb, an adverb, a subject, and so on and so
forth). ConvNets are also not well suited for "Entity Extraction," where we need
to understand where relevant entities are located in sentences. Indeed, it turns out
that a position is pretty useful information for both of the last two use cases. 1D
ConvNets are very similar to 2D ConvNets. However, the former operates on a
single vector, while the latter operates on matrices.
Using a CNN for sentiment analysis
Let's have a look at the code. First of all, we load the dataset with tensorflow_
datasets. In this case we use IMDb, a collection of movie reviews:
import tensorflow as tf
from tensorflow.keras import datasets, layers, models, preprocessing
import tensorflow_datasets as tfds
max_len = 200
n_words = 10000
dim_embedding = 256
EPOCHS = 20
BATCH_SIZE =500
def load_data():
#load data
(X_train, y_train), (X_test, y_test) = datasets.imdb.load_
data(num_words=n_words)
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