Natural Language Processing in R for Beginners

Natural Language Processing in R for Beginners, available at $44.99, has an average rating of 4.5, with 70 lectures, based on 21 reviews, and has 116 subscribers.

You will learn about 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 This course is ideal for individuals who are Data scientists looking to branch out to NLP or Business analysts who need to get insight from text data or Hobbyists who want to explore the interesting world of text analysis It is particularly useful for Data scientists looking to branch out to NLP or Business analysts who need to get insight from text data or Hobbyists who want to explore the interesting world of text analysis.

Enroll now: Natural Language Processing in R for Beginners

Summary

Title: Natural Language Processing in R for Beginners

Price: $44.99

Average Rating: 4.5

Number of Lectures: 70

Number of Published Lectures: 70

Number of Curriculum Items: 70

Number of Published Curriculum Objects: 70

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: What To Expect

Lecture 2: How To Use Course Textbook

Chapter 2: APIs with jsonlite

Lecture 1: What is an API?

Lecture 2: GET Requests

Lecture 3: API Call in R Studio with jsonlite

Lecture 4: API Keys

Lecture 5: Hands-On Practice with Pokemon API

Lecture 6: Hands-On Practice with OMDb API

Chapter 3: Twitter Data with rtweet

Lecture 1: Applying for Twitter Developer Account

Lecture 2: Getting Twitter API Keys

Lecture 3: Searching for Tweets and Getting Timelines

Lecture 4: Hands-On Practice with rtweet

Chapter 4: Web Scraping with rvest

Lecture 1: Introduction to Web Scraping and rvest

Lecture 2: Scraping a Table from Webpage

Lecture 3: Scraping Data from Unstructured Sources

Lecture 4: Hands-On Practice with rvest

Chapter 5: Getting Wikipedia Data with getwiki

Lecture 1: Introduction to getwiki

Lecture 2: Getting Text from Wikipedia Article

Lecture 3: Searching for Articles

Lecture 4: Hands-On Practice with getwiki

Chapter 6: Regex and Stringr

Lecture 1: Introduction to Regex

Lecture 2: Introduction to stringr

Lecture 3: Basic Pattern Matching in Regex

Lecture 4: Anchors and Optional Characters

Lecture 5: Ranges and Repeating Patterns

Lecture 6: Hands-On Practice with stringr

Chapter 7: Preparing Text Data with Tidytext

Lecture 1: Introduction to Tokenization

Lecture 2: Tokenizing n-grams

Lecture 3: Removing Stop Words

Lecture 4: Stemming

Lecture 5: Lemmatization

Lecture 6: Cleaning n-grams

Lecture 7: Hands-On Practice with Preparing Text Data

Chapter 8: Visualize Text Data

Lecture 1: Creating Bar Charts and Word Clouds

Lecture 2: Hands-On Practice with Exploring Text Data

Chapter 9: Working in tm

Lecture 1: Creating a Corpus

Lecture 2: Inspecting a Corpus

Lecture 3: Data Cleaning

Lecture 4: Using a Document Term Matrix

Chapter 10: Term Frequency – Inverse Document Frequency (TF-IDF)

Lecture 1: Introduction to TF-IDF

Lecture 2: Term Frequency

Lecture 3: Inverse Document Frequency

Lecture 4: Applying TF-IDF

Lecture 5: Hands-On Practice with TF-IDF

Chapter 11: Sentiment Analysis

Lecture 1: Introduction to Sentiment Analysis

Lecture 2: Lexicons

Lecture 3: Presenting Sentiments in Word Clouds

Lecture 4: Making a Radar Chart

Lecture 5: Analyzing Changing Sentiment with a Bar Chart

Lecture 6: Hands-On Practice with Sentiment Analysis

Chapter 12: Topic Modeling

Lecture 1: Introduction to Topic Modeling and Latent Dirichlet Allocation (LDA)

Lecture 2: Word-Topic Dirichlet Distribution

Lecture 3: LDA in R

Lecture 4: Interpreting the Word Topic Distribution

Lecture 5: Interpreting the Document Topic Distribution

Chapter 13: Parts of Speech Tagging and Name Entity Recognition

Lecture 1: Parts of Speech Tagging and Name Entity Recognition Overview

Lecture 2: Introduction to Parts of Speech Tagging

Lecture 3: udpipe

Lecture 4: Tagging a Sentence

Lecture 5: Tagging a Wikipedia Article

Lecture 6: Tagging a Wikipedia Article (cont.)

Lecture 7: Note on Installing Java

Lecture 8: Name Entity Recognition (NER) and openNLP

Lecture 9: Preparing for NER

Lecture 10: Annotating Entities

Lecture 11: Performing NER on Wikipedia Article

Chapter 14: Word Embeddings

Lecture 1: Introduction to Word Embeddings

Lecture 2: GloVe Part 1

Lecture 3: GloVe Part 2

Lecture 4: GloVe Part 3

Instructors

Michael Bunting
Data Scientist
Corydon Baylor
Data Scientist
Caitlin Kennedy
Data Scientist
Hassan Mahmood
Data Scientist

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 2 votes
4 stars: 3 votes
5 stars: 15 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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