Bag of Words
Understanding the BoW Model
As we have already mentioned in the previous chapter, the bag of words (BoW) model represents documents as vectors where each dimension corresponds to a unique word. Each dimension can either represent the presence of a word within the document (1 if present, 0 if absent) or its frequency (word count). Therefore, BoW models can be either binary or frequency-based.
Let's take a look at how the same sentence (document) is represented by each type:
As you can see, a binary model represents this document as the [1, 1, 1] vector, while frequency-based models represent it as [2, 1, 2], taking word frequency into account.
BoW Implementation
Let's now delve into the BoW model implementation in Python. Implementing the BoW model can be a straightforward process, especially with the help of the sklearn (Scikit-learn) library and its CountVectorizer class.
Without further ado, let's proceed with an example of a binary bag of words:
Code Description
from sklearn.feature_extraction.text import CountVectorizerThis line imports the
CountVectorizer class from sklearn.corpus = [
'Global climate change poses significant risks to global ecosystems.',
'Global warming and climate change demand urgent action.',
'Sustainable environmental practices support environmental conservation.',
]These lines define a list named
corpus that contains three string elements. Each string is a text document. vectorizer = CountVectorizer(binary=True)This line creates an instance of the
CountVectorizer class. The binary=True parameter specifies that the model should use binary counts. bow_matrix = vectorizer.fit_transform(corpus)The method first fits the model to the data, learning the vocabulary of the corpus, and then transforms the text documents into a sparse matrix of token counts (bag of words matrix).
print(bow_matrix.toarray())This line converts the sparse matrix
bow_matrix into a dense array (numpy.ndarray) using the .toarray() method and prints it.
Each row in the matrix corresponds to a document, and each column to a token (word). In order to visually represent it, we converted this sparse matrix into a dense 2D array using the .toarray() method.
In order to create a frequency-based bag of words model, all we have to do is remove the parameter binary=True since the default value for it is False:
Converting the Matrix to a DataFrame
It can be quite convenient to convert the resulting bag of words matrix into a pandas DataFrame. Moreover, the CountVectorizer instance offers the get_feature_names_out() method, which retrieves an array of unique words (feature names) used in the model. These feature names can set as the columns of the resulting DataFrame, here is an example:
With this representation, we can now easily access not only the vector for a particular document, but the vector of a particular word:
Since each unique word corresponds to a column, accessing a word vector is as simple as accessing a column in the DataFrame by specifying the word (for example, 'global'). We also use the values attribute to obtain an array instead of a Series as the result.
Tudo estava claro?
Conteúdo do Curso
Introduction to NLP
2. Stemming and Lemmatization
Introduction to NLP
Bag of Words
Understanding the BoW Model
As we have already mentioned in the previous chapter, the bag of words (BoW) model represents documents as vectors where each dimension corresponds to a unique word. Each dimension can either represent the presence of a word within the document (1 if present, 0 if absent) or its frequency (word count). Therefore, BoW models can be either binary or frequency-based.
Let's take a look at how the same sentence (document) is represented by each type:
As you can see, a binary model represents this document as the [1, 1, 1] vector, while frequency-based models represent it as [2, 1, 2], taking word frequency into account.
BoW Implementation
Let's now delve into the BoW model implementation in Python. Implementing the BoW model can be a straightforward process, especially with the help of the sklearn (Scikit-learn) library and its CountVectorizer class.
Without further ado, let's proceed with an example of a binary bag of words:
Code Description
from sklearn.feature_extraction.text import CountVectorizerThis line imports the
CountVectorizer class from sklearn.corpus = [
'Global climate change poses significant risks to global ecosystems.',
'Global warming and climate change demand urgent action.',
'Sustainable environmental practices support environmental conservation.',
]These lines define a list named
corpus that contains three string elements. Each string is a text document. vectorizer = CountVectorizer(binary=True)This line creates an instance of the
CountVectorizer class. The binary=True parameter specifies that the model should use binary counts. bow_matrix = vectorizer.fit_transform(corpus)The method first fits the model to the data, learning the vocabulary of the corpus, and then transforms the text documents into a sparse matrix of token counts (bag of words matrix).
print(bow_matrix.toarray())This line converts the sparse matrix
bow_matrix into a dense array (numpy.ndarray) using the .toarray() method and prints it.
Each row in the matrix corresponds to a document, and each column to a token (word). In order to visually represent it, we converted this sparse matrix into a dense 2D array using the .toarray() method.
In order to create a frequency-based bag of words model, all we have to do is remove the parameter binary=True since the default value for it is False:
Converting the Matrix to a DataFrame
It can be quite convenient to convert the resulting bag of words matrix into a pandas DataFrame. Moreover, the CountVectorizer instance offers the get_feature_names_out() method, which retrieves an array of unique words (feature names) used in the model. These feature names can set as the columns of the resulting DataFrame, here is an example:
With this representation, we can now easily access not only the vector for a particular document, but the vector of a particular word:
Since each unique word corresponds to a column, accessing a word vector is as simple as accessing a column in the DataFrame by specifying the word (for example, 'global'). We also use the values attribute to obtain an array instead of a Series as the result.
Tudo estava claro?
