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.

Import NLTK

List Comprehension

Regexp Tokenizer

Data Visualization

Зміст курсу

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

Import NLTK

The first step is to import the nltk library into our Python environment. This is your gateway to a plethora of text processing tools and techniques that NLTK offers.

Once nltk is successfully imported, we will take an important step in our project - defining the text that we will analyze and process. For this purpose, we'll be using a variable named story. This variable will hold the textual data that you wish to work on. It could be any piece of text - a short story, an excerpt from a book, a paragraph of your choice, or even a collection of sentences that you're interested in analyzing.

Note

The first task's tests are tailored to function with the initial text provided. Therefore, if you choose to use a different text for analysis, it's okay to disregard any errors that might arise from the initial test.

Завдання

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Import the NLTK (Natural Language Toolkit) library into your Python environment.

Note

If you want to use nltk on your computer, you need to install it first using the following command:

Mark tasks as Completed

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The first step is to import the nltk library into our Python environment. This is your gateway to a plethora of text processing tools and techniques that NLTK offers.

Once nltk is successfully imported, we will take an important step in our project - defining the text that we will analyze and process. For this purpose, we'll be using a variable named story. This variable will hold the textual data that you wish to work on. It could be any piece of text - a short story, an excerpt from a book, a paragraph of your choice, or even a collection of sentences that you're interested in analyzing.

Note

The first task's tests are tailored to function with the initial text provided. Therefore, if you choose to use a different text for analysis, it's okay to disregard any errors that might arise from the initial test.

Завдання

Swipe to show code editor

Import the NLTK (Natural Language Toolkit) library into your Python environment.

Note

If you want to use nltk on your computer, you need to install it first using the following command:

Mark tasks as Completed

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Секція 1. Розділ 2

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