Bag of Words vs Tfidf : Which is Better?
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to understand, interpret, and generate human language. One of the fundamental tasks in NLP is text representation, where textual data is converted into numerical form so that machine learning algorithms can process it effectively. Two of the most common text representation techniques are Bag of Words (BoW) and TF-IDF (Term Frequency-Inverse Document Frequency). While both approaches aim to transform text into a structured format, they do so in different ways and have unique advantages and disadvantages.
This article provides an in-depth comparison of BoW vs. TF-IDF, including their working mechanisms, advantages, disadvantages, and when to use each technique.
1. Understanding Bag of Words (BoW)
What is Bag of Words?
Bag of Words is a simple and effective technique for text representation. It converts text into a numerical format by counting the frequency of each word in a document while completely ignoring grammar, word order, and context.
How BoW Works
- Tokenization: The text is broken into words (tokens).
- Vocabulary Creation: A set of unique words from the text corpus is created.
- Word Frequency Calculation: Each word’s frequency in a document is counted.
Consider two simple sentences:
- Sentence 1: “I love NLP.”
- Sentence 2: “NLP is amazing.”
The vocabulary extracted from these sentences would be:["I", "love", "NLP", "is", "amazing"]
Using BoW, we create the following representation:
| I | love | NLP | is | amazing | |
|---|---|---|---|---|---|
| Sent1 | 1 | 1 | 1 | 0 | 0 |
| Sent2 | 0 | 0 | 1 | 1 | 1 |
Each row represents a sentence, and each column represents a word from the vocabulary. The values indicate the count of each word in the respective sentence.
Advantages of BoW
✅ Simple and efficient – It is easy to implement and requires minimal preprocessing.
✅ Works well for small datasets – If the dataset is small, BoW can provide decent results.
✅ Useful for document classification – It helps in tasks like spam detection and sentiment analysis.
Disadvantages of BoW
❌ Ignores word meaning and context – The order of words is lost, which can impact understanding.
❌ High dimensionality – The size of the vocabulary determines the size of the matrix, leading to memory inefficiency for large datasets.
❌ Does not differentiate between important and common words – Frequent words like “is”, “the”, and “and” may dominate the representation.
2. Understanding TF-IDF (Term Frequency-Inverse Document Frequency)
What is TF-IDF?
TF-IDF is an advanced technique that improves upon BoW by assigning importance to words based on their frequency within a document while reducing the weight of words that appear frequently across all documents.
How TF-IDF Works
TF-IDF consists of two components:
- Term Frequency (TF): Measures how often a word appears in a document. TF=Number of times the word appears in a documentTotal words in the documentTF = \frac{\text{Number of times the word appears in a document}}{\text{Total words in the document}}TF=Total words in the documentNumber of times the word appears in a document
- Inverse Document Frequency (IDF): Measures how important a word is by considering how many documents contain it. IDF=log(Total number of documentsNumber of documents containing the word)IDF = \log \left(\frac{\text{Total number of documents}}{\text{Number of documents containing the word}} \right)IDF=log(Number of documents containing the wordTotal number of documents)
The final TF-IDF Score is computed as:TF−IDF=TF×IDFTF-IDF = TF \times IDFTF−IDF=TF×IDF
Example Calculation
Consider a corpus with three sentences:
- Sentence 1: “NLP is great.”
- Sentence 2: “NLP is amazing.”
- Sentence 3: “NLP is the future.”
Step 1: Compute TF (Term Frequency)
Let’s assume each word appears only once per sentence, and each sentence has three words. The term frequency (TF) for each word is:
- TF(“NLP”) = 1/3
- TF(“is”) = 1/3
- TF(“great”) = 1/3
- TF(“amazing”) = 1/3
- TF(“future”) = 1/3
- TF(“the”) = 1/3
Step 2: Compute IDF (Inverse Document Frequency)
Since “NLP” and “is” appear in every sentence, their IDF will be low. However, “great”, “amazing”, and “future” appear only once, so they get a higher IDF score.
- IDF(“NLP”) = log(3/3) = 0
- IDF(“is”) = log(3/3) = 0
- IDF(“great”) = log(3/1) ≈ 1.1
- IDF(“amazing”) = log(3/1) ≈ 1.1
- IDF(“future”) = log(3/1) ≈ 1.1
- IDF(“the”) = log(3/1) ≈ 1.1
Final TF-IDF Scores:
- Common words (“NLP”, “is”) get a low score.
- Unique words (“great”, “amazing”, “future”) get a higher score.
Advantages of TF-IDF
✅ Gives weight to important words – Rare but meaningful words get a higher score.
✅ Reduces the impact of common words – Words that appear frequently across all documents receive lower importance.
✅ Better document similarity – Helps improve search engines and text classification tasks.
Disadvantages of TF-IDF
❌ Still a sparse representation – Large vocabularies still result in large matrices.
❌ Computationally expensive for large datasets – Computing IDF requires scanning all documents.
❌ Ignores word meaning – Like BoW, TF-IDF doesn’t capture word relationships or context.
3. Key Differences Between BoW and TF-IDF
| Feature | Bag of Words (BoW) | TF-IDF |
|---|---|---|
| Weighting | Pure word count | Weighted by importance |
| Handles Common Words | Treats all words equally | Downweights frequent words |
| Computational Complexity | Lower | Higher |
| Handles Context | No | No |
| Better for Classification | Works but less effective | More effective |
4. When to Use BoW vs. TF-IDF
- Use BoW if:
- You need a simple, interpretable model.
- You have a small dataset and don’t require word weighting.
- You are performing basic document classification.
- Use TF-IDF if:
- You want to emphasize important words while downplaying common words.
- You need better results in search engines and text classification.
- You have a moderate-sized dataset and can afford additional computation.
5. Beyond BoW and TF-IDF
While both techniques are useful, modern NLP has moved towards word embeddings like:
- Word2Vec – Captures semantic relationships.
- GloVe – Uses global co-occurrence.
- BERT – Uses deep learning for contextual understanding.
These methods preserve meaning, relationships, and context, making them superior for tasks like sentiment analysis, chatbot development, and text generation.
Conclusion
BoW and TF-IDF are foundational NLP techniques for text vectorization. BoW is simpler and faster, while TF-IDF improves relevance by reducing the impact of common words. However, for advanced NLP tasks, deep learning-based word embeddings outperform both.
Would you like a Python code example to compare BoW and TF-IDF in action? 🚀