Advantages of Bag of Words
1️⃣ Simple and Intuitive
Bag of Words is easy to understand because we only count words.
2. Fixed-Size Input (Good for ML Algorithms)
Fixed-size vector means every sentence is represented using the same number of values.
No matter how long or short the sentence is.
Suppose after preprocessing, your vocabulary is:
[good, boy, girl, food, pizza]
Vocabulary size = 5
Now Bag of Words says:
👉 Every sentence must be represented using 5 numbers.
Sentence 1:
good boy
Vector:
[1 1 0 0 0]
(5 values)
Sentence 2:
pizza food
Vector:
[0 0 0 1 1]
(5 values)
Sentence 3:
good girl pizza
Vector:
[1 0 1 0 1]
(5 values)
⭐ Important:
Even though sentences have different numbers of words,
their vectors are always:
length = 5
Because vocabulary size = 5.
Why is this useful?
Machine Learning models expect:
👉 same input size every time
Example:
If model expects 5 features:
[x1 x2 x3 x4 x5]
Every input must have exactly 5 values.
Bag of Words guarantees this.
So:
ML algorithms can directly accept BoW vectors.
No extra processing needed.
Disadvantages of Bag of Words
1️⃣ Sparse Matrix → Overfitting
Most values are zero.
Explain:
Too many zeros confuse ML models and can cause overfitting.
Same problem as One-Hot.
2. Word Order is Lost
Simple Example
Consider these two sentences:
Sentence 1:
The food is good
Sentence 2:
The food is not good
After removing stopwords and applying Bag of Words:
Vocabulary:
[food, good, not]
BoW vectors:
Sentence 1:
[1 1 0]
Sentence 2:
[1 1 1]
The vectors are almost the same.
But meaning is totally different:
- Sentence 1 → Positive
- Sentence 2 → Negative
Yet Bag of Words treats them as very similar.
3️⃣ Out-of-Vocabulary (OOV) Problem
New word appears → BoW cannot represent it.
Example:
Vocabulary:
food, pizza, burger
Test word:
pasta
No column → word ignored.
4️⃣ Semantic Meaning is Not Captured
BoW treats:
- good
- excellent
as unrelated.
Also:
- pizza
- burger
are treated independently.
Explain:
BoW does not understand meaning or similarity.