Скачать или смотреть Using CountVectorizer with Custom Vocabulary N-grams in Python

Learn how to use scikit-learn's `CountVectorizer` with custom vocabulary and n-grams to detect combinations of words in your text.
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Understanding Custom Vocabulary N-grams in CountVectorizer

When it comes to text analysis in Python, the CountVectorizer from scikit-learn is a powerful tool. However, users sometimes run into issues when they want to move beyond single words and detect phrases or combinations of words, also known as n-grams. In this guide, we'll tackle the problem of how to create a custom vocabulary of n-grams, specifically focusing on detecting the phrase "big dog" in your text.

The Problem at Hand

Your goal is to detect combinations of words, such as "big dog", rather than just individual words. The initial attempt might look like this:

[[See Video to Reveal this Text or Code Snippet]]

The output from this code was not as expected; it didn't detect the phrase "big dog". Instead, it provided an output array indicating the presence of the word "cat" but not the desired combination.

The Misunderstanding

This problem arises from the default behavior of the CountVectorizer, which is set to recognize single words (i.e., n-grams of size 1). If you want your CountVectorizer to look for pairs of words, you need to adjust its settings. Let's explore how to resolve this issue effectively.

The Solution: Adjusting N-gram Range

To capture combinations of words, you should define an n-gram range that accommodates your needs. Here's how you can do this:

Step 1: Define N-gram Range

You need to set the ngram_range parameter in the CountVectorizer. By specifying a range of (1, 2), you instruct the vectorizer to consider both single words and combinations of two words. This allows it to recognize "big dog" as a valid n-gram.

Step 2: Update Your Code

Here's the revised code that includes the n-gram range setting:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

Now, when you run this code, you should see an output array that indicates the presence of both "big dog" and "cat". You can expect something similar to this:

[[See Video to Reveal this Text or Code Snippet]]

This output means that one instance of "big dog" and one instance of "cat" was detected in your given text.

Conclusion

By modifying the ngram_range parameter in CountVectorizer, you can create a custom vocabulary that includes phrases as well as individual words. This makes it a versatile tool in text analysis for identifying specific combinations of words. So the next time you're ready to analyze text data with phrases, remember to adjust your n-gram settings! Happy coding!