CountVectorizer in Python
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.
Code
The code below shows how to use CountVectorizer in Python.
from sklearn.feature_extraction.text import CountVectorizer# list of text documentstext = ["John is a good boy. John watches basketball"]vectorizer = CountVectorizer()# tokenize and build vocabvectorizer.fit(text)print(vectorizer.vocabulary_)# encode documentvector = vectorizer.transform(text)# summarize encoded vectorprint(vector.shape)print(vector.toarray())
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