Bag of words vs One hot encoding: Which is Better?
Both Bag of Words (BoW) and One-Hot Encoding (OHE) are text vectorization techniques used in Natural Language Processing (NLP), but they differ in how they represent words.
1. Overview of Bag of Words (BoW)
Bag of Words is a frequency-based representation of text where each document is converted into a vector of word counts.
How BoW Works
- Tokenization – Split text into words.
- Create a Vocabulary – Store unique words.
- Vectorization – Convert text into numerical form by counting word occurrences.
Example BoW Representation
Sentences:
- “I love NLP”
- “NLP is amazing”
| I | love | NLP | is | amazing | |
|---|---|---|---|---|---|
| Sent1 | 1 | 1 | 1 | 0 | 0 |
| Sent2 | 0 | 0 | 1 | 1 | 1 |
Advantages of BoW
✅ Simple and easy to implement
✅ Useful for text classification and sentiment analysis
Disadvantages of BoW
❌ Ignores word order and meaning
❌ Creates sparse, high-dimensional vectors
2. Overview of One-Hot Encoding (OHE)
One-Hot Encoding represents each word as a unique binary vector where only one element is 1, and the rest are 0.
How OHE Works
- Create a Vocabulary – Store unique words.
- Vectorization – Assign a binary vector to each word.
Example OHE Representation
Vocabulary: {I, love, NLP, is, amazing}
| Word | I | love | NLP | is | amazing |
|---|---|---|---|---|---|
| “I” | 1 | 0 | 0 | 0 | 0 |
| “love” | 0 | 1 | 0 | 0 | 0 |
| “NLP” | 0 | 0 | 1 | 0 | 0 |
| “is” | 0 | 0 | 0 | 1 | 0 |
| “amazing” | 0 | 0 | 0 | 0 | 1 |
Advantages of OHE
✅ Preserves unique word identity
✅ Useful for categorical text data
Disadvantages of OHE
❌ Ignores word frequency and order
❌ Creates very high-dimensional vectors for large vocabularies
3. Key Differences Between BoW and OHE
| Feature | Bag of Words (BoW) | One-Hot Encoding (OHE) |
|---|---|---|
| Definition | Counts word occurrences | Unique binary representation for each word |
| Output Type | Integer counts | Binary vectors |
| Handles Word Frequency? | Yes | No |
| Handles Multiple Words in a Sentence? | Yes | No |
| Dimensionality | High (but lower than OHE) | Very High (1 vector per word) |
| Word Order Consideration? | No | No |
| Use Cases | Text classification, sentiment analysis | Word embeddings, categorical data representation |
4. When to Use BoW vs. OHE
- Use BoW if:
✅ You need a document-level word representation.
✅ Word frequency matters for your NLP task.
✅ You are working on text classification. - Use OHE if:
✅ You need a word-level unique representation.
✅ You are working with categorical text data (e.g., token classification).
✅ You plan to use embeddings like Word2Vec or BERT later.
Conclusion
- BoW counts word occurrences in a document.
- OHE assigns a unique binary vector to each word.
👉 For text classification, BoW is better. For categorical word representation, OHE is useful! 🚀