Text Mining and Natural Language Processing in R

Text Mining and Natural Language Processing in R, available at $84.99, has an average rating of 4.65, with 85 lectures, based on 828 reviews, and has 9151 subscribers.

You will learn about Students will be able to read in data from different sources- including databases Basic webscraping- extracting text and tabular data from HTML pages Social media mining from Facebook and Twitter Extract information relating to tweets and posts Analyze text data for emotions Carry out Sentiment analysis Implement natural language processing (NLP) on different types of text data This course is ideal for individuals who are People who wish to learn practical text mining and natural language processing or People with prior experience of using RStudio or People with some prior experience of implementing machine learning techniques in R or People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course or People who wish to derive insights from textual and social media data It is particularly useful for People who wish to learn practical text mining and natural language processing or People with prior experience of using RStudio or People with some prior experience of implementing machine learning techniques in R or People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course or People who wish to derive insights from textual and social media data.

Enroll now: Text Mining and Natural Language Processing in R

Summary

Title: Text Mining and Natural Language Processing in R

Price: $84.99

Average Rating: 4.65

Number of Lectures: 85

Number of Published Lectures: 84

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 85

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Students will be able to read in data from different sources- including databases
Basic webscraping- extracting text and tabular data from HTML pages
Social media mining from Facebook and Twitter
Extract information relating to tweets and posts
Analyze text data for emotions
Carry out Sentiment analysis
Implement natural language processing (NLP) on different types of text data

Who Should Attend

People who wish to learn practical text mining and natural language processing
People with prior experience of using RStudio
People with some prior experience of implementing machine learning techniques in R
People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course
People who wish to derive insights from textual and social media data

Target Audiences

People who wish to learn practical text mining and natural language processing
People with prior experience of using RStudio
People with some prior experience of implementing machine learning techniques in R
People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course
People who wish to derive insights from textual and social media data

Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media?

Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends?

Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally, you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data.

TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL

BECOME AN EXPERT IN TEXT MINING & NATURAL LANGUAGE PROCESSING :

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like the caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data. I will even introduce you to some very important practical case studies – such as identifying important words in a text and predicting movie sentiments based on textual reviews. You will also extract tweets pertaining to trending topics analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful course, you’ll know it all: extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning!

Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

Data Structures and Reading in R, including CSV, Excel, JSON, HTML data.
Web-Scraping using R
Extracting text data from Twitter and Facebook using APIs
Extract and clean data from the FourSquare app
Exploratory data analysis of textual data
Common Natural Language Processing techniques such as sentiment analysis and topic modelling
Implement machine learning techniques such as clustering, regression and classification on textual data
Network analysis

Plus you will apply your newly gained skills and complete a practical text analysis assignment

We will spend some time dealing with some of the theoretical concepts. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results.

After each video, you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Lecture 1: About the Course and Instructor

Lecture 2: Data and Scripts For the Course

Lecture 3: Introduction to R and RStudio

Lecture 4: Conclusion to Section 1

Chapter 2: Reading in Data from Different Sources

Lecture 1: Read in CSV & Excel Data

Lecture 2: Read in Data from Online CSV

Lecture 3: Read in Zipped File

Lecture 4: Read Data from a Database

Lecture 5: Read in JSON Data

Lecture 6: Read in Data from PDF Documents

Lecture 7: Read in Tables from PDF Documents

Lecture 8: Conclusion to Section 2

Chapter 3: Webscraping: Extract Data from Webpages

Lecture 1: Read in Data From Online Google Sheets

Lecture 2: Read in Data from Online HTML Tables-Part 1

Lecture 3: Read in Data from Online HTML Tables-Part 2

Lecture 4: Get and Clean Data from HTML Tables

Lecture 5: Read Text Data from an HTML Page

Lecture 6: Introduction to Selector Gadget

Lecture 7: More Webscraping With rvest-IMDB Webpage

Lecture 8: Another Way of Accessing Webpage Elements

Lecture 9: Conclusions to Section 3

Chapter 4: Introduction to APIs

Lecture 1: What is an API?

Lecture 2: Extract Text Data from Guardian Newspaper

Chapter 5: Text Data Mining from Social Media

Lecture 1: Extract Data from Facebook

Lecture 2: Get More out Of Facebook

Lecture 3: Set up a Twitter App for Mining Data from Twitter

Lecture 4: Extract Tweets Using R

Lecture 5: More Twitter Data Extraction Using R

Lecture 6: Get Tweet Locations

Lecture 7: Get Location Specific Trends

Lecture 8: Learn More About the Followers of a Twitter Handle

Lecture 9: Another Way of Extracting Information From Twitter- the rtweet Package

Lecture 10: Geolocation Specific Tweets With "rtweet"

Lecture 11: More Data Extraction Using rtweet

Lecture 12: Locations of Tweets

Lecture 13: Mining Github Using R

Lecture 14: Set up the FourSquare App

Lecture 15: Extract Reviews for Venues on FourSquare

Lecture 16: Conclusions to Section 5

Chapter 6: Exploring Text Data For Preliminary Ideas

Lecture 1: Explore Tweet Data

Lecture 2: A Brief Explanation

Lecture 3: EDA With Text Data

Lecture 4: Examine Multiple Document Corpus of Text

Lecture 5: Brief Introduction to tidytext

Lecture 6: Text Exploration & Visualization with tidytext

Lecture 7: Explore Multiple Texts with tidytext

Lecture 8: Count Unique Words in Tweets

Lecture 9: Visualizing Text Data as TF-IDF

Lecture 10: TF-IDF in Graphical Form

Lecture 11: Conclusions to Section 6

Chapter 7: Natural Language Processing: Sentiment Analysis

Lecture 1: Wordclouds for Visualizing Tweet Sentiments: India's Demonetization Policy

Lecture 2: Wordclouds for Visualizing Reviews

Lecture 3: Tidy Wordclouds

Lecture 4: Quanteda Wordcloud

Lecture 5: Word Frequency in Text Data

Lecture 6: Tweet Sentiments- Mugabe's Ouster

Lecture 7: Tidy Sentiments- Sentiment Analysis Using tidytext

Lecture 8: Examine the Polarity of Text

Lecture 9: Examine the Polarity of Tweets

Lecture 10: Topic Modelling a Document

Lecture 11: Topic Modelling Multiple Documents

Lecture 12: Topic Modelling Tweets Using Quanteda

Lecture 13: Conclusions to Section 7

Chapter 8: Text Data and Machine Learning

Lecture 1: Clustering for Text Data

Lecture 2: Clustering Tweets with Quanteda

Lecture 3: Regression on Text Data

Lecture 4: Identify Spam Emails with Supervised Classification

Lecture 5: Introduction to RTextTools

Lecture 6: More on RTextTools

Lecture 7: Classifying Textual Data

Lecture 8: ML Approaches For Predicting a Binary Outcome in Text Data

Lecture 9: ML Approaches For Predicting a Multi-Class Outcome in Text Data

Chapter 9: Network Analysis

Lecture 1: A Small (Social) Network

Lecture 2: A More Theoretical Explanation

Lecture 3: Build & Visualize a Network

Lecture 4: Network of Emails

Lecture 5: More on Network Visualization

Lecture 6: Analysis of Tweet Network

Lecture 7: Identify Word Pair Networks

Lecture 8: Network of Words

Chapter 10: Miscellaneous Lectures

Lecture 1: Github

Lecture 2: Using R Within Google Colab

Lecture 3: What Is Data Science?

Lecture 4: Data Editing

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 28 votes
2 stars: 45 votes
3 stars: 115 votes
4 stars: 234 votes
5 stars: 406 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

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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