Natural Language Processing in R for Beginners
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
- Preview01:20
- 00:42How To Use Course Textbook
Requirements
- Basic Understanding of R
- Desire to Learn Natural Language Processing
- Bonus: Knowledge of the tidyverse
Description
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!
Who this course is for:
- Data scientists looking to branch out to NLP
- Business analysts who need to get insight from text data
- Hobbyists who want to explore the interesting world of text analysis
Instructors
![Michael Bunting](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 5.0 Instructor Rating
- 3 Reviews
- 15 Students
- 1 Course
I am a data scientist at a Fortune 500 company. After years of experience in industry, I decided to share some of my professional experience in data science and analytics. I focus on creating understandable content that can be consumed by both newcomers and experienced professionals. I have experience in R, Python, and Tableau and am passionate about sharing what I've learned.
- 5.0 Instructor Rating
- 3 Reviews
- 15 Students
- 1 Course
I am a data scientist at a Fortune 500 company. My focus has been a combination of creating client ready models and explaining those models for a non technical audience.
As far as programming languages go, I mostly work in R but I know python and some javascript as well.
I think data science should be intuitive and fun. I'm excited to learn with you.
- 5.0 Instructor Rating
- 3 Reviews
- 15 Students
- 1 Course
I am currently a data scientist with a background in mathematics. I have experience working both as a data scientist consultant and as a mathematics TA for university courses as well as a high school tutor. Additionally, I had a Fulbright Scholarship to teach English in a public high school abroad. My background in education coupled with my analytics skills has prepared me to excitedly work on creating some Udemy courses!
- 5.0 Instructor Rating
- 3 Reviews
- 15 Students
- 1 Course
Hi I’m Hassan, and I have a weird fascination with everything technology and in particular coding. My day to day job is being a Data Scientist for a large consulting firm and my favorite programming language of choice is Python. My favorite quote is “Software is eating the world” by Marc Andreessen.