Text Preprocessing in NLP: Bag of Words (BoW) and TF-IDF

2026-06-09 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, short

Summary

Text preprocessing is a crucial step in Natural Language Processing (NLP) that converts raw text into numerical formats for machine learning models. Two foundational techniques for this are Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). BoW represents text by counting word occurrences, ignoring grammar and order, and is easy to implement for basic text classification but creates sparse matrices and treats all words equally. TF-IDF improves upon BoW by weighting words based on their importance across a corpus, assigning higher scores to words frequent in one document but rare overall, thereby reducing the influence of common words and often improving model performance. While TF-IDF highlights important words, both methods do not understand semantics or word order, unlike modern embeddings such as Word2Vec or BERT. The article provides a comparison and Python implementation using `CountVectorizer` and `TfidfVectorizer` from `sklearn`.

Key takeaway

For Machine Learning Engineers building NLP systems, understanding Bag of Words (BoW) and TF-IDF is crucial for foundational text feature engineering. While modern embeddings exist, these techniques provide a robust baseline for converting text into numerical features, improving model accuracy. You should consider BoW for simple classification tasks due to its ease of implementation, and TF-IDF for better performance where word importance is key, leveraging `sklearn`'s `CountVectorizer` and `TfidfVectorizer` for efficient implementation.

Key insights

Bag of Words and TF-IDF are foundational text vectorization techniques essential for converting human language into machine-readable numerical features in NLP.

Principles

• Text data needs numerical conversion for ML.
• Preprocessing improves model accuracy.
• TF-IDF weights words by importance.

Method

The NLP pipeline involves cleaning, tokenization, and then feature extraction using techniques like Bag of Words (BoW) or TF-IDF to convert text into numerical features for machine learning models.

In practice

• Use `CountVectorizer` for BoW.
• Use `TfidfVectorizer` for TF-IDF.
• Apply BoW for basic text classification.

Topics

• Natural Language Processing
• Text Preprocessing
• Bag of Words
• TF-IDF
• Feature Engineering
• sklearn

Best for: AI Student, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.