Python Natural Language Processing (NLP) Basics


Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human languages. In this comprehensive guide, we'll explore the basics of NLP with Python. You'll learn about text preprocessing, tokenization, part-of-speech tagging, and how to perform common NLP tasks using Python libraries.


Before you begin, make sure you have the following prerequisites in place:

  • Python Installed: You should have Python installed on your local development environment.
  • Python Libraries: Install essential NLP libraries like NLTK or spaCy.
  • Basic Python Knowledge: Familiarity with Python programming is essential.

Step 1: Text Preprocessing

Text preprocessing is the first step in NLP. It involves cleaning and preparing text data for analysis.

Sample Code for Text Preprocessing

Perform basic text preprocessing using Python:

text = "This is an example sentence for text preprocessing."
# Lowercase the text
text = text.lower()
# Remove punctuation
text = ''.join([c for c in text if c.isalpha() or c.isspace()])

Step 2: Tokenization

Tokenization is the process of breaking text into individual words or tokens.

Sample Code for Tokenization

Tokenize a sentence using Python:

from nltk.tokenize import word_tokenize
tokens = word_tokenize("This is tokenization example.")


Python NLP basics provide a solid foundation for text analysis, sentiment analysis, and more. This guide has introduced you to the fundamentals, but there's a wide range of NLP techniques, models, and applications to explore as you delve deeper into the field of natural language processing.