Learn all the details to create stunning visualizations for text data and your NLP projects in Python!
Natural Language Processing, or NLP, is a very popular subfield in Data Science at the moment because it allows computers to process and analyze human language. Siri and Alexa, spam filters, chatbots, auto-complete, and translate apps are all examples of everyday technology that use NLP.
As a Data Scientist, working with text data is a bit trickier than other types of data. Why? Because words are not numbers! This makes the Exploratory Data Analysis and the data cleaning and preprocessing steps a bit different in the Data Science workflow. Text data generally requires much more cleaning (removing stop words and punctuation, lowercasing, stemming or lemmatizing, etc). It also requires tokenizing or vectorizing the text (deriving meaningful numbers from words). As for exploring and analyzing the data, there are not as many ways to visualize text. However, text does open up one new kind of visualization technique that you have probably seen before — word clouds.
During my latest Data Science project, I got to delve into the world of NLP. Along the way, I learned all about creating word clouds in Python, and I wanted to write this piece to share my knowledge for anyone looking to create some beautiful visualizations for text data.
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Visualizing Your Data from MongoDB to Word Cloud with Python 3: Word clouds have become more and more popular nowadays as it is a straightforward and efficient way to communicate data.
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In this part, I figured out several ways to present and create a Word Cloud using different methods — Tableau, Python, and Google World Cloud Generator.
Methods to extract meaningful words from a text and create word cloud art in python.