Text Mining and Natural Language Processing in R

Hands-on text mining and natural language processing (NLP) training for data science applications in R

4.60 (818 reviews)
Udemy
platform
English
language
Data Science
category
instructor
9,065
students
9 hours
content
Nov 2022
last update
$84.99
regular price

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

Description

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 Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models.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!



Content

INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

About the Course and Instructor
Data and Scripts For the Course
Introduction to R and RStudio
Conclusion to Section 1

Reading in Data from Different Sources

Read in CSV & Excel Data
Read in Data from Online CSV
Read in Zipped File
Read Data from a Database
Read in JSON Data
Read in Data from PDF Documents
Read in Tables from PDF Documents
Conclusion to Section 2

Webscraping: Extract Data from Webpages

Read in Data From Online Google Sheets
Read in Data from Online HTML Tables-Part 1
Read in Data from Online HTML Tables-Part 2
Get and Clean Data from HTML Tables
Read Text Data from an HTML Page
Introduction to Selector Gadget
More Webscraping With rvest-IMDB Webpage
Another Way of Accessing Webpage Elements
Conclusions to Section 3

Introduction to APIs

What is an API?
Extract Text Data from Guardian Newspaper

Text Data Mining from Social Media

Extract Data from Facebook
Get More out Of Facebook
Set up a Twitter App for Mining Data from Twitter
Extract Tweets Using R
More Twitter Data Extraction Using R
Get Tweet Locations
Get Location Specific Trends
Learn More About the Followers of a Twitter Handle
Another Way of Extracting Information From Twitter- the rtweet Package
Geolocation Specific Tweets With "rtweet"
More Data Extraction Using rtweet
Locations of Tweets
Mining Github Using R
Set up the FourSquare App
Extract Reviews for Venues on FourSquare
Conclusions to Section 5

Exploring Text Data For Preliminary Ideas

Explore Tweet Data
A Brief Explanation
EDA With Text Data
Examine Multiple Document Corpus of Text
Brief Introduction to tidytext
Text Exploration & Visualization with tidytext
Explore Multiple Texts with tidytext
Count Unique Words in Tweets
Visualizing Text Data as TF-IDF
TF-IDF in Graphical Form
Conclusions to Section 6

Natural Language Processing: Sentiment Analysis

Wordclouds for Visualizing Tweet Sentiments: India's Demonetization Policy
Wordclouds for Visualizing Reviews
Tidy Wordclouds
Quanteda Wordcloud
Word Frequency in Text Data
Tweet Sentiments- Mugabe's Ouster
Tidy Sentiments- Sentiment Analysis Using tidytext
Examine the Polarity of Text
Examine the Polarity of Tweets
Topic Modelling a Document
Topic Modelling Multiple Documents
Topic Modelling Tweets Using Quanteda
Conclusions to Section 7

Text Data and Machine Learning

Clustering for Text Data
Clustering Tweets with Quanteda
Identify Spam Emails with Supervised Classification
Introduction to RTextTools
More on RTextTools
Classifying Textual Data
ML Approaches For Predicting a Binary Outcome in Text Data
ML Approaches For Predicting a Multi-Class Outcome in Text Data

Network Analysis

A Small (Social) Network
A More Theoretical Explanation
Build & Visualize a Network
Network of Emails
More on Network Visualization
Analysis of Tweet Network
Identify Word Pair Networks
Network of Words

Screenshots

Text Mining and Natural Language Processing in R - Screenshot_01Text Mining and Natural Language Processing in R - Screenshot_02Text Mining and Natural Language Processing in R - Screenshot_03Text Mining and Natural Language Processing in R - Screenshot_04

Reviews

Eric
October 17, 2023
n'est plus à jour pour twitter n'est plus à jour pour facebook (pourrait donner plus de détail sur comment crée un compte et connecter l'API
Yael
July 18, 2023
I tried following the previous exercise but the package used is outdated and the functions don't work the same way anymore! Unfortunately, the course needs constant updating.
Jaskaran
May 21, 2023
The course is very useful in the sense that it contains valuable information about the usage of R for Text mining and Natural language processing.
Kamlesh
March 18, 2023
The course offers valuable insights into text mining and natural language processing in R. The instructor is well-versed with the concept.
Anonymized
February 4, 2023
The instructor has brilliantly explained the utility and importance of R in text mining and natural language processing.
Jansen
December 22, 2022
I recently completed a Text Mining and Natural Language Processing course in R and it was amazing! The professor was great at breaking down complex topics into simple terms. The material was simple, but thorough and useful. I had no prior knowledge of the subject but by the end of the course I felt confident with the basics and confident enough to apply to a real-world situation. I highly suggest this course for anyone interested in learning about text mining and natural language processing in R.
Stipe
October 24, 2022
There are enough explanation for code and examples, there should be more content for downloading in the section
Anonymized
October 18, 2022
The instructor is at her best while explaining finer nuances of text mining and natural processing in R.
Paola
September 6, 2022
Quien da el curso conoce el tema, pero la mitad de las librerías que usa ya no funcionan. Las carpetas que proporciona están desordenadas, por lo que es difícil seguir los ejemplos. Habría sido mejor que las clases se compartieran en proyectos, lo cual haría totalmente replicables sus ejemplos. Otra deficiencia del curso es que no explica las funciones, ni la sintaxis del código
Sweta
July 10, 2022
The instructor's knowledge of the subject regarding data processing in R and her delivery is impressive.
Mario
April 23, 2022
The course contains valuable information on text mining and natural language processing..The concept has been brilliantly explained by the instructor.
Anonymized
April 12, 2022
Hands-on course on text mining and natural language processing training for data applications in R. It has vast potential for practical applications.
Kathryn
April 6, 2022
First of all, the instructor does not adequately explain concepts. This course is basically a video of the instructor walking through and executing lines of code that she has written. It includes absolutely zero explanation of why she wrote the code the way she did. Why did she choose a particular function? How is this code doing what we want it to? If you want to know any of this, be prepared to teach yourself. Second, some of the code doesn't run, making it extremely difficult to follow the 'hands on training'. Based on my research, it is because this course is using packages that have not been maintained and updated (XML and rcurl, to be specific).
Pieter
March 4, 2022
Although I'm already familiar with R and RStudio, I still learnt a bit more. But I look forward to the new material. I'm glad to have heard in the introduction that the lecturer is a good teacher.
Ramakrishna
February 1, 2022
Hand-0n course and excellent use of R for data mining and natural language processing. Brilliant presentation by the instructor.

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1470544
udemy ID
12/15/2017
course created date
6/17/2019
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