Introduction to NLP with NLTK and spaCy

Natural Language Processing (NLP) has become a core part of many modern applications, from chatbots and recommendation systems to sentiment analysis and language translation. In Python, two of the most popular libraries for NLP are NLTK (Natural Language Toolkit) and spaCy. Both libraries offer a range of tools to help process, analyze, and understand text data.

In this post, we’ll explore the basics of NLTK and spaCy, their differences, and how you can use them to start working with NLP tasks.

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Text summarization example using spaCy

The idea is to extract sentences that contain important keywords or those that are highly related to the core message of the text. We can use spaCy to tokenize the text, identify entities and nouns, and assign scores to each sentence based on the frequency of important words.

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