Bag of Words (CountVectorizer) in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Bag of Words model in text processing?

It is a way to represent text by counting how many times each word appears, ignoring grammar and word order.

beginner

What does CountVectorizer do in NLP?

CountVectorizer converts a collection of text documents into a matrix of word counts, showing how often each word appears in each document.

beginner

Why does Bag of Words ignore word order?

Because it focuses only on the frequency of words, not their position, to simplify text into numbers for machine learning.

intermediate

How does CountVectorizer handle new words not seen during training?

It ignores words not in its vocabulary, so new words in test data are not counted in the output matrix.

intermediate

What is a limitation of the Bag of Words model?

It loses the meaning from word order and context, so it can’t understand phrases or sentence structure.

What does CountVectorizer output for a set of text documents?

A A matrix of word counts per document

B A list of sentences

C A summary of text meaning

D A list of synonyms

CountVectorizer creates a matrix showing how many times each word appears in each document.

Which aspect does Bag of Words ignore?

A Word frequency

B Word spelling

C Word order

D Word count

Bag of Words counts words but does not consider the order they appear in.

If a new word appears in test data but not in training, what happens in CountVectorizer?

A It ignores the word

B It counts the word normally

C It adds the word to the vocabulary

D It throws an error

CountVectorizer ignores words not in its learned vocabulary.

Why is Bag of Words useful for machine learning?

A It translates text into another language

B It converts text into numbers that models can understand

C It summarizes text meaning

D It corrects grammar mistakes

Machine learning models need numbers, and Bag of Words turns text into numeric counts.

Which is a common problem with Bag of Words?

A It requires labeled data

B It needs a lot of memory for small texts

C It only works with numbers

D It loses context and word order

Bag of Words does not keep the order or meaning of words, only counts.

Explain how CountVectorizer transforms text data into a format usable by machine learning models.

Think about how text is turned into numbers by counting words.

Describe one main limitation of the Bag of Words model and why it matters.

Consider what information is lost when only counting words.

Practice

1. What does the Bag of Words model do in text processing?

easy

A. Counts how often each word appears in the text

B. Translates text into another language

C. Removes all punctuation from the text

D. Generates summaries of the text

Solution

1. Step 1: Understand Bag of Words purpose

   Bag of Words counts the frequency of each word in a text, ignoring order.

2. Step 2: Compare options to definition

   Only Counts how often each word appears in the text matches this description exactly.

3. Final Answer:

   Counts how often each word appears in the text -> Option A

4. Quick Check:

   Bag of Words = Counts words [OK]

Hint: Bag of Words counts words, not translates or summarizes [OK]

Common Mistakes:

• Confusing Bag of Words with translation
• Thinking it removes punctuation only
• Assuming it summarizes text

2. Which of the following is the correct way to import CountVectorizer from scikit-learn in Python?

easy

A. import CountVectorizer from sklearn.feature_extraction

B. from sklearn.feature_extraction.text import CountVectorizer

C. from sklearn.text import CountVectorizer

D. import CountVectorizer from sklearn.text

Solution

1. Step 1: Recall correct import path

   CountVectorizer is in sklearn.feature_extraction.text module.

2. Step 2: Match options to correct syntax

   Only from sklearn.feature_extraction.text import CountVectorizer uses the correct 'from ... import ...' syntax and correct module path.

3. Final Answer:

   from sklearn.feature_extraction.text import CountVectorizer -> Option B

4. Quick Check:

   Correct import path = from sklearn.feature_extraction.text import CountVectorizer [OK]

Hint: CountVectorizer is in sklearn.feature_extraction.text [OK]

Common Mistakes:

• Using wrong module path
• Incorrect import syntax
• Trying to import from sklearn.text

3. What will be the output shape of the matrix after applying CountVectorizer on these two sentences:
['I love cats', 'Cats love me']?

medium

A. (3, 2)

B. (2, 3)

C. (4, 2)

D. (2, 4)

Solution

1. Step 1: Identify unique words

   Words are: 'I', 'love', 'cats', 'me' (case insensitive, 'Cats' and 'cats' same).

2. Step 2: Count sentences and features

   There are 2 sentences and 4 unique words, so matrix shape is (2, 4).

3. Final Answer:

   (2, 4) -> Option D

4. Quick Check:

   2 sentences, 4 words = (2, 4) [OK]

Hint: Count unique words and sentences for shape (rows, columns) [OK]

Common Mistakes:

• Counting words per sentence instead of unique words
• Mixing rows and columns in shape
• Ignoring case sensitivity

4. The following code throws an error. What is the mistake?

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

medium

A. get_feature_names() is deprecated, should use get_feature_names_out()

B. fit_transform() should be fit_transform_text()

C. toarray() is not a method of X

D. CountVectorizer() needs a parameter for language

Solution

1. Step 1: Identify deprecated method

   get_feature_names() is deprecated in recent sklearn versions.

2. Step 2: Use correct method

   Replace get_feature_names() with get_feature_names_out() to fix error.

3. Final Answer:

   get_feature_names() is deprecated, should use get_feature_names_out() -> Option A

4. Quick Check:

   Use get_feature_names_out() not get_feature_names() [OK]

Hint: Use get_feature_names_out() instead of deprecated get_feature_names() [OK]

Common Mistakes:

• Thinking fit_transform() is wrong
• Assuming toarray() is invalid
• Believing CountVectorizer needs language parameter

5. You have a list of sentences with some words repeated many times. How can you use CountVectorizer to ignore words that appear in more than 50% of the sentences?

hard

A. Set min_df=0.5 to ignore frequent words

B. Use stop_words='english' to remove frequent words

C. Set the parameter max_df=0.5 when creating CountVectorizer

D. Set max_features=0.5 to limit word count

Solution

1. Step 1: Understand max_df parameter

   max_df=0.5 tells CountVectorizer to ignore words in more than 50% of documents.

2. Step 2: Compare other options

   min_df controls minimum frequency, stop_words removes common English words, max_features limits number of features, none ignore frequent words by percentage.

3. Final Answer:

   Set the parameter max_df=0.5 when creating CountVectorizer -> Option C

4. Quick Check:

   max_df filters frequent words by document frequency [OK]

Hint: Use max_df to exclude very common words [OK]

Common Mistakes:

• Confusing max_df with min_df
• Thinking stop_words removes all frequent words
• Using max_features to filter frequency