Document-term matrix in NLP

Introduction

A document-term matrix helps us turn text into numbers so computers can understand and learn from it.

When you want to analyze the words used in a collection of documents.

When building a search engine to find documents by keywords.

When preparing text data for machine learning models like spam detection.

When comparing how similar two documents are based on their words.

When summarizing the frequency of words across many texts.

Syntax

NLP

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(documents)

# dtm is a matrix where rows are documents and columns are words
# dtm[i, j] shows how many times word j appears in document i

CountVectorizer converts text to a matrix of word counts.

The fit_transform method learns the vocabulary and creates the matrix in one step.

Examples

This creates a matrix showing word counts for two sentences.

NLP

documents = ["I love cats", "Cats love fish"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(documents)
print(dtm.toarray())

This removes common English words like 'I' before counting.

NLP

vectorizer = CountVectorizer(stop_words='english')
dtm = vectorizer.fit_transform(documents)
print(vectorizer.get_feature_names_out())

Sample Model

This program turns three sentences into a matrix showing how often each word appears in each sentence.

NLP

from sklearn.feature_extraction.text import CountVectorizer

# Sample documents
documents = [
    "Machine learning is fun",
    "Learning machines be fun",
    "Fun with machine learning"
]

# Create the vectorizer
vectorizer = CountVectorizer()

# Fit and transform the documents into a document-term matrix
dtm = vectorizer.fit_transform(documents)

# Show the feature names (words)
print("Words:", vectorizer.get_feature_names_out())

# Show the document-term matrix as an array
print("Document-Term Matrix:\n", dtm.toarray())

Important Notes

The document-term matrix is usually very sparse because most words don't appear in every document.

You can use other vectorizers like TfidfVectorizer to weigh words differently.

Summary

A document-term matrix changes text into numbers by counting words.

It helps computers understand and compare documents.

CountVectorizer from scikit-learn is a simple way to create this matrix.

Practice

1. What does a document-term matrix represent in natural language processing?

easy

A. The length of each document

B. The order of words in a sentence

C. The meaning of each word

D. Counts of words in each document

Solution

1. Step 1: Understand the purpose of a document-term matrix

   A document-term matrix counts how many times each word appears in each document.

2. Step 2: Compare options with this definition

   Only Counts of words in each document correctly describes this counting process.

3. Final Answer:

   Counts of words in each document -> Option D

4. Quick Check:

   Document-term matrix = word counts [OK]

Hint: Remember: matrix counts words per document [OK]

Common Mistakes:

• Confusing word order with counts
• Thinking it shows word meanings
• Assuming it measures document length

2. Which Python library provides the CountVectorizer class to create a document-term matrix?

easy

A. numpy

B. pandas

C. scikit-learn

D. matplotlib

Solution

1. Step 1: Recall the library for text feature extraction

   CountVectorizer is part of scikit-learn, a popular machine learning library.

2. Step 2: Verify other options

   numpy is for arrays, pandas for data frames, matplotlib for plotting, so they don't provide CountVectorizer.

3. Final Answer:

   scikit-learn -> Option C

4. Quick Check:

   CountVectorizer = scikit-learn [OK]

Hint: CountVectorizer is from scikit-learn, not numpy [OK]

Common Mistakes:

• Choosing numpy because it handles arrays
• Confusing pandas with text vectorization
• Selecting matplotlib for visualization

3. What is the output of this Python code snippet?

from sklearn.feature_extraction.text import CountVectorizer
texts = ['cat dog', 'dog dog cat']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
print(X.toarray())

medium

A. [[1 1]
[1 2]]

B. [[1 1]
[2 1]]

C. [[2 1]
[1 2]]

D. [[1 2]
[1 1]]

Solution

1. Step 1: Identify the vocabulary and word counts

   The texts are 'cat dog' and 'dog dog cat'. Vocabulary sorted alphabetically is ['cat', 'dog']. First document has 1 'cat' and 1 'dog'. Second document has 1 'cat' and 2 'dog's.

2. Step 2: Form the document-term matrix

   Matrix rows correspond to documents, columns to words: [[1,1],[1,2]].

3. Final Answer:

   [[1 1] [1 2]] -> Option A

4. Quick Check:

   Word counts match matrix [OK]

Hint: Count words per document in alphabetical order [OK]

Common Mistakes:

• Mixing order of words in vocabulary
• Counting wrong number of word occurrences
• Confusing rows and columns

4. Identify the error in this code that tries to create a document-term matrix:

from sklearn.feature_extraction.text import CountVectorizer
texts = ['apple orange', 'orange apple apple']
vectorizer = CountVectorizer()
X = vectorizer.transform(texts)
print(X.toarray())

medium

A. toarray() is not a method of X

B. Missing fit() before transform()

C. texts should be a single string, not a list

D. CountVectorizer() should be CountVector()

Solution

1. Step 1: Understand CountVectorizer usage

   CountVectorizer requires calling fit() or fit_transform() before transform() to learn vocabulary.

2. Step 2: Check the code sequence

   The code calls transform() directly without fit(), causing an error.

3. Final Answer:

   Missing fit() before transform() -> Option B

4. Quick Check:

   fit() needed before transform() [OK]

Hint: Always fit before transform with CountVectorizer [OK]

Common Mistakes:

• Skipping fit() step
• Using wrong class name
• Passing wrong data type to vectorizer

5. You have three documents: ['sun moon', 'moon moon sun', 'star sun moon']. Using CountVectorizer, what is the shape of the document-term matrix and which word has the highest total count across all documents?

hard

A. Shape (3, 3), 'moon' has highest count

B. Shape (3, 4), 'sun' has highest count

C. Shape (3, 3), 'sun' has highest count

D. Shape (3, 4), 'moon' has highest count

Solution

1. Step 1: Identify unique words and matrix shape

   Unique words are 'sun', 'moon', 'star' -> 3 words. There are 3 documents, so shape is (3, 3).

2. Step 2: Count total occurrences of each word

   'sun': appears 1 + 1 + 1 = 3 times 'moon': appears 1 + 2 + 1 = 4 times 'star': appears 0 + 0 + 1 = 1 time Highest count is 'moon' with 4.

3. Final Answer:

   Shape (3, 3), 'moon' has highest count -> Option A

4. Quick Check:

   3 docs x 3 words, moon count highest [OK]

Hint: Count unique words for shape, sum counts for highest word [OK]

Common Mistakes:

• Counting duplicate words as unique
• Mixing up shape dimensions
• Incorrectly summing word counts