CountVectorizer to Extract Options from Texts in Python, in Element | by Rashida Nasrin Sucky | Oct, 2023

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All the things you’ll want to know to make use of CountVectorizer effectively in Sklearn

Probably the most primary information processing that any Pure Language Processing (NLP) mission requires is to transform the textual content information to the numeric information. So long as the information is in textual content kind we can not do any type of computation motion on it.

There are a number of strategies out there for this text-to-numeric information conversion. This tutorial will clarify one of the primary vectorizers, the CountVectorizer technique within the scikit-learn library.

This technique may be very easy. It takes the frequency of prevalence of every phrase because the numeric worth. An instance will make it clear.

Within the following code block:

  • We’ll import the CountVectorizer technique.
  • Name the strategy.
  • Match the textual content information to the CountVectorizer technique and, convert that to an array.
import pandas as pd 
from sklearn.feature_extraction.textual content import CountVectorizer

#That is the textual content to be vectorized
textual content = ["Hello Everyone! This is Lilly. My aunt's name is also Lilly. I love my aunt.
I am trying to learn how to use count vectorizer."]

cv= CountVectorizer()
count_matrix = cv.fit_transform(textual content)
cnt_arr = count_matrix.toarray()


array([[1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1]],

Right here I’ve the numeric values representing the textual content information above.

How do we all know which values signify which phrases within the textual content?

To make that clear, it will likely be useful to transform the array right into a DataFrame the place column names would be the phrases themselves.

cnt_df = pd.DataFrame(information = cnt_arr, columns = cv.get_feature_names())

Now, it reveals clearly. The worth of the phrase ‘additionally’ is 1 which suggests ‘additionally’ appeared solely as soon as within the take a look at. The phrase ‘aunt’ got here twice within the textual content. So, the worth of the phrase ‘aunt’ is 2.

Within the final instance, all of the sentences have been in a single string. So, we obtained just one row of information for 4 sentences. Let’s rearrange the textual content and…

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