Bag of Words (BoW) and TF-IDF in Natural Language Processing
Machine learning models cannot understand raw text directly. To process text,
we must convert words into numerical representations. Bag of Words (BoW) and
TF-IDF are two of the most fundamental techniques used to transform text data
into numbers.
These techniques are widely used in traditional NLP systems and still play an
important role in tasks such as text classification, spam detection, and search engines.
⭐ What is Text Vectorization?
Text vectorization is the process of converting textual data into numerical vectors
that machine learning algorithms can process. Each document is represented as a
vector of numbers instead of words.
📌 Bag of Words (BoW)
Bag of Words is a simple representation where a document is described by the
frequency of words present in it. Grammar and word order are ignored.
How Bag of Words Works:
- Create a vocabulary of all unique words
- Count word occurrences in each document
- Represent each document as a frequency vector
Example:
from sklearn.feature_extraction.text import CountVectorizer
documents = [
"AI is powerful",
"AI is the future",
"Deep learning is powerful"
]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(documents)
print(vectorizer.get_feature_names_out())
print(X.toarray())
Advantages of BoW:
- Simple and easy to understand
- Fast to implement
- Works well for small datasets
Limitations of BoW:
- Ignores word meaning and context
- Vocabulary size can become very large
- All words are treated equally
📌 TF-IDF (Term Frequency – Inverse Document Frequency)
TF-IDF improves Bag of Words by giving more importance to rare and meaningful
words while reducing the weight of very common words.
TF-IDF Concept:
- Term Frequency (TF): How often a word appears in a document
- Inverse Document Frequency (IDF): How rare a word is across documents
TF-IDF Formula:
TF-IDF = TF × IDF
Python Example:
from sklearn.feature_extraction.text import TfidfVectorizer
documents = [
"AI is powerful",
"AI is the future",
"Deep learning is powerful"
]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(documents)
print(vectorizer.get_feature_names_out())
print(X.toarray())
Why TF-IDF is Better than BoW:
- Reduces importance of common words
- Highlights meaningful keywords
- Improves model accuracy
📌 Comparison: BoW vs TF-IDF
- BoW: Counts word frequency only
- TF-IDF: Considers word importance across documents
📌 Real-Life Applications
- Spam email detection
- Search engine ranking
- Document similarity analysis
- News classification systems
📌 Project Title
Document Similarity and Keyword Extraction System
📌 Project Description
In this project, you will build a system using TF-IDF to measure similarity between
documents and extract important keywords. This project forms the foundation for
search engines and recommendation systems.
📌 Summary
Bag of Words and TF-IDF are essential building blocks of NLP. While BoW provides
a simple representation, TF-IDF enhances it by weighting words based on importance.
These techniques are widely used and serve as a strong base before moving to
word embeddings and deep learning models.
