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Day 34 - Natural Language Processing (NLP) with NLTK

Hello and welcome back to the Python for DevOps series! On Day 34, we're going to discuss Natural Language Processing (NLP) using the Natural Language Toolkit, or NLTK. If you've ever wondered how machines can understand and process human language, you're in for a treat!

What is Natural Language Processing?

In simple terms, NLP is the technology that empowers machines to understand, interpret, and generate human-like text. Think of it as a bridge between the languages we speak and the ones computers understand. NLTK, a powerful library in Python, makes diving into NLP a breeze.

Installing NLTK

Before we jump into the exciting examples, let's ensure you have NLTK installed. Open your terminal and type:

pip install nltk

Great! Now, let's explore a few key aspects of NLP with NLTK.


One of the first steps in NLP is breaking down a text into smaller units called tokens. These tokens can be as small as words or even individual characters. NLTK's tokenizer makes this process seamless:

from nltk.tokenize import word_tokenize

sentence = "NLTK makes NLP a walk in the park!"
tokens = word_tokenize(sentence)


The output should be a list of tokens:

['NLTK', 'makes', 'NLP', 'a', 'walk', 'in', 'the', 'park', '!']

Stopwords Removal

Stopwords are common words like "is," "the," and "and" that don't carry much meaning. Removing them can improve the efficiency of our NLP algorithms:

from nltk.corpus import stopwords

stop_words = set(stopwords.words("english"))
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]


This code snippet will output:

['NLTK', 'makes', 'NLP', 'walk', 'park', '!']

Frequency Distribution

Understanding the frequency of words in a text is essential. NLTK simplifies this task with its handy FreqDist class:

from nltk import FreqDist

freq_distribution = FreqDist(filtered_tokens)

The result will display the frequency distribution of words in your text.

Part-of-Speech Tagging

NLTK can also tag each word in a sentence with its part of speech (POS). This information is crucial for extracting meaning from the text:

from nltk import pos_tag

pos_tags = pos_tag(filtered_tokens)

The output will be a list of tuples, where each tuple contains a word and its corresponding part of speech.

Named Entity Recognition (NER)

NER involves identifying entities like names, locations, and organizations in a text. NLTK simplifies this process:

from nltk import ne_chunk

ner_tags = ne_chunk(pos_tags)

The output will be a tree structure highlighting the named entities in your text.

Congratulations! You've just scratched the surface of NLP using NLTK. As you continue your Python for DevOps journey, remember that NLP plays a crucial role in automating tasks involving human language. From chatbots to sentiment analysis, NLTK opens up a world of possibilities.

On Day 35, we'll explore another exciting aspect of Python for DevOps.

Thank you for reading!

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