Bag of words vs Term Frequency: Which is Better?
Both Bag of Words (BoW) and Term Frequency (TF) are text representation techniques used in Natural Language Processing (NLP), but they differ in how they handle word importance in documents.
1. Overview of Bag of Words (BoW)
BoW is a basic count-based model that represents text as a word frequency matrix, ignoring grammar and word order.
How BoW Works
- Tokenization – Split text into words.
- Vocabulary Creation – Store all unique words.
- Vectorization – Count the occurrences of each word in each document.
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
✅ Works well for text classification
✅ Computationally inexpensive
Disadvantages of BoW
❌ Ignores word order and meaning
❌ Fails to capture word importance across documents
❌ Generates sparse, high-dimensional matrices
2. Overview of Term Frequency (TF)
Term Frequency (TF) improves BoW by normalizing word counts based on document length. Instead of raw counts, it represents the importance of a word in a document.
TF Formula:
TF=Number of times word appears in documentTotal number of words in documentTF = \frac{\text{Number of times word appears in document}}{\text{Total number of words in document}}TF=Total number of words in documentNumber of times word appears in document
Example Using TF
Sentences:
- “I love NLP NLP.”
- “NLP is amazing.”
| I | love | NLP | is | amazing | |
|---|---|---|---|---|---|
| Sent1 | 0.25 | 0.25 | 0.5 | 0 | 0 |
| Sent2 | 0 | 0 | 0.33 | 0.33 | 0.33 |
Advantages of TF
✅ Accounts for document length
✅ Gives more weight to frequently occurring words within a document
✅ Improves BoW by making word importance more meaningful
Disadvantages of TF
❌ Does not consider word importance across multiple documents
❌ Common words (e.g., “the”, “is”) may still dominate text representation
3. Key Differences Between BoW and TF
| Feature | Bag of Words (BoW) | Term Frequency (TF) |
|---|---|---|
| Definition | Counts occurrences of words | Normalizes word count by document length |
| Output | Integer counts | Relative word frequency |
| Handles Document Length? | No | Yes |
| Captures Word Importance? | No | Yes (but only within a document) |
| Word Order Consideration? | No | No |
| Use Cases | Text classification, sentiment analysis | Improved BoW-based models |
4. When to Use BoW vs. TF
- Use BoW if:
✅ You need a simple count-based text representation.
✅ Your dataset consists of short, uniform-length documents.
✅ You are working on basic NLP tasks like spam detection or topic modeling. - Use TF if:
✅ You need a more refined BoW model that considers document length.
✅ You want to reduce bias from document length variations.
✅ You are working on tasks where word importance matters (e.g., search engines).
Conclusion
- BoW is a simple word count method that does not account for word importance.
- TF normalizes word counts to give better importance to words within a document.
👉 If document length varies in your dataset, TF is a better choice than BoW! 🚀