Natural Language Processing in R for Beginners

Learn NLP in R with our easy to understand videos and free textbook!

Working with text data does not need to be difficult!

What you’ll learn

  • Access Text Data from APIs with jsonlite.
  • Scrape the Web Using rvest.
  • Import Data from Twitter and Wikipedia.
  • Find Patterns using Regex.
  • Manipulate and Clean Data Using tidytext and tm.
  • Measure Emotion with Sentiment Analysis.
  • Surface Meaning with Topic Modeling.
  • Provide Context with Parts of Speech Tagging and Named Entity Recognition.
  • Quantify Relationships with Word Embeddings.

Course Content

  • Introduction –> 2 lectures • 2min.
  • APIs with jsonlite –> 6 lectures • 11min.
  • Twitter Data with rtweet –> 4 lectures • 12min.
  • Web Scraping with rvest –> 4 lectures • 11min.
  • Getting Wikipedia Data with getwiki –> 4 lectures • 9min.
  • Regex and Stringr –> 6 lectures • 20min.
  • Preparing Text Data with Tidytext –> 7 lectures • 18min.
  • Visualize Text Data –> 2 lectures • 10min.
  • Working in tm –> 4 lectures • 16min.
  • Term Frequency – Inverse Document Frequency (TF-IDF) –> 5 lectures • 13min.

Natural Language Processing in R for Beginners

Requirements

  • Basic Understanding of R.
  • Desire to Learn Natural Language Processing.
  • Bonus: Knowledge of the tidyverse.

Working with text data does not need to be difficult!

Follow along as we explain complex topics for a beginner audience. By the end of this course, you will be able to read in data from websites like twitter and wikipedia, clean it, and perform analysis.

We keep it easy.

This course is designed for a data analyst who is familiar with the R language but has absolutely no background in natural language processing or even statistics in general.

We break our course into three main sections: text mining, preparing and exploring text data, and analyzing text data.

Text Mining

Like with every other form of analytics, before any real work can be done, the data must exist (obviously) and be in a working format.

What’s Covered: APIs, Twitter Data, Webscraping, Wikipedia Data

Preparing and Exploring Text Data

Once the data has been properly gathered and mined, it needs to be put into a usable format. The following tutorials cover how to clean and explore text data.

What’s Covered: Regex, stringr package, tidytext package, tm package

Analyzing Text Data

After exploratory data analysis has been performed, we can do further analysis of the relationships and meaning in text.

What’s Covered: TF-IDF, Sentiment Analysis, Topic Modeling, Parts of Speech Tagging, Name Entity Recognition, Word Embeddings

 

So dive in and see what insights are hiding in your text data!