Text Feature Extraction Made Simple

Beginner’s Guide to Text Vectorization for NLP Tasks

Nikita Prasad

May 31, 2025

In most of the cases, text classification systems primarily have two main parts:

Feature Extraction Component (Turning Text into Numbers):
1. This part takes a piece of text and turns it into a set of features or characteristics (basically, numbers).
2. These features help the system understand what's important in the text.
Classifier/ Regressor Component (Making a Decision):
1. Once the features are generated, this part of the system uses them to decide what category label or fine-grained scores the text belongs to.
2. It matches the features with a list of known categories/ scores and assigns the most appropriate one to the text.

By the end of this read, you will have a solid understanding of:

What Text Vectorization is?
Why it is essential while working with Text data? and,
What popular techniques you can use?

This is helpful for day-to-day work as a Data Scientist.

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Let’s dive in!

What is Feature Extraction from Text or Text Vectorization?

Machine learning algorithms require numerical input.

Hence, we required to use Text Vectorization to converts textual data into numerical format, enabling algorithms to process and analyze it effectively.

Why do we need Feature Extraction from Text?

Text vectorization captures important information from sequential text data.

It includes word frequency, relationships between words, semantic and syntactic meaning.

Simply stating,

Which words are common?
Which words are rare?
How words are connected?
What the sentence might mean?

All of this helps the machine learn and make better predictions.

Text Feature Extraction Techniques

Here's a comparison table of common Text Feature Extraction Techniques used in Natural Language Processing (NLP).

Tips for Use:

Want to try these techniques? Here’s how to choose:

Choose Word2Vec / FastText / GloVe for capturing semantic relationships.
Use BERT or Transformer-based models when context is critical (e.g., QA, NER).