bag of words

If we want to use text in Machine Learning algorithms, we’ll have to convert then to a numerical representation. It should be no surprise that computers are very well at handling numbers.

We convert text to a numerical representation called a feature vector. A feature vector can be as simple as a list of numbers.

The bag-of-words model is one of the feature extraction algorithms for text.

Feature extraction from text

The bag of words model ignores grammar and order of words.
We start with two documents (the corpus):

‘All my cats in a row’,
‘When my cat sits down, she looks like a Furby toy!’,

 
A list in then created based on the two strings above:

{‘all’: 0, ‘cat’: 1, ‘cats’: 2, ‘down’: 3, ‘furby’: 4, ‘in’: 5, ‘like’: 6, ‘looks’: 7, ‘my’: 8, ‘row’: 9, ‘she’: 10, ‘sits’: 11, ‘toy’: 12, ‘when’: 13 }

 
The list contains 14 unique words: the vocabulary. That’s why every document is represented by a feature vector of 14 elements. The number of elements is called the dimension.

Then we can express the texts as numeric vectors:

[[1 0 1 0 0 1 0 0 1 1 0 0 0 0]
[0 1 0 1 1 0 1 1 1 0 1 1 1 1]]

 
Lets take a closer look:

‘All my cats in a row’ = [1 0 1 0 0 1 0 0 1 1 0 0 0 0]

 
If we follow the order of the vocabulary:
 
bag of words, feature extraction

we’ll get a vector, the bag of words representation.

Bag of words code

We’ll define a collection of strings called a corpus. Then we’ll use the CountVectorizer to create vectors from the corpus.

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