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CountVectorizer in Python

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In order to use textual data for predictive modeling, the text must be parsed to remove certain words – this process is called tokenization. These words need to then be encoded as integers, or floating-point values, for use as inputs in machine learning algorithms. This process is called feature extraction (or vectorization).

Scikit-learn’s CountVectorizer is used to convert a collection of text documents to a vector of term/token counts. It also enables the ​pre-processing of text data prior to generating the vector representation. This functionality makes it a highly flexible feature representation module for text.

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Code

The code below shows how to use CountVectorizer in Python.

from sklearn.feature_extraction.text import CountVectorizer

# list of text documents
text = ["John is a good boy. John watches basketball"]

vectorizer = CountVectorizer()
# tokenize and build vocab
vectorizer.fit(text)

print(vectorizer.vocabulary_)

# encode document
vector = vectorizer.transform(text)
# summarize encoded vector
print(vector.shape)
print(vector.toarray())

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