Identifying the Most Frequent Words in Text

Identifying the Most Frequent Words in Text

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In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data. Our focus will encompass several core areas of natural language processing: tokenization, stemming, tagging and parsing. These NLTK features will form the backbone of our text processing and analysis tasks, making it an essential tool in our project for handling and extracting meaningful insights from language data.

List Comprehension

Regexp Tokenizer

Data Visualization

Course Content

Identifying the Most Frequent Words in Text

Identifying the Most Frequent Words in Text

Introduction Import NLTK Tokenization Stopwords List Comprehension Stemming Tagging Lemmatizer Regexp Tokenizer Data Visualization Word Cloud

List Comprehension

List comprehension in Python is a compact and efficient method for generating a new list. This technique involves applying an expression to each item in an existing list, and it may include a filter condition to selectively process elements. This approach is a shorthand method, enhancing both the readability and efficiency of your code.

Task

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Create a list comprehension to generate a list of words from story that are not considered stopwords.

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List comprehension in Python is a compact and efficient method for generating a new list. This technique involves applying an expression to each item in an existing list, and it may include a filter condition to selectively process elements. This approach is a shorthand method, enhancing both the readability and efficiency of your code.

Task

Swipe to show code editor

Create a list comprehension to generate a list of words from story that are not considered stopwords.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 5

AVAILABLE TO ULTIMATE ONLY

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