Examples

Examples

Cleaning a body of text mined from an API

In this short example, we will be looking at cleaning a body of text mined from an API, to prepare your text for further processing and feature engineering like Sentiment Analysis etc. With cleantweet, it is easy to clean a body of text with just a few lines of code:

Install the cleantweet library

!pip install cleantweet

Import the cleantweet library

import cleantweet as clt

Create an instance of the CleanTweet class

data = clt.CleanTweet('mini corpus for testing')

The text document before using cleantweet

Call the clean() method on the object

data = data.clean()

View the cleaned body of text

print(data)

The text document after using cleantweet

Creating a Wordcloud

You can use the DiagramTweet class to create different diagrams for NLP tasks. To create a Wordcloud using cleantweet, takes two lines of code:

figures = clt.DiagramTweet('mini corpus for testing.txt') figures.word_cloud()

The first line creates an instance of the DiagramTweet class and the second line calls the word_cloud() method. To know more about the different [parameters of the word_cloud() method, you can see it's full definition in the API docs