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Text Mining and Natural Language Processing in R

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 li...

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Course Description

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 caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packag...

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 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 and 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.

What you’ll learn
  • 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
  • Exploratory data analysis of textual data
  • Network analysis

Covering Topics

1
Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
2
Section 2 : Reading in Data from Different Sources
3
Section 3 : Webscraping: Extract Data from Webpages
4
Section 4 : Introduction to APIs
5
Section 5 : Text Data Mining from Social Media
6
Section 6 : Exploring Text Data For Preliminary Ideas
7
Section 7 : Natural Language Processing: Sentiment Analysis
8
Section 8 : Text Data and Machine Learning
9
Section 9 : Network Analysis

Curriculum

      Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
1
About the Course and Instructor Preview
2
Data and Scripts For the Course
3
Introduction to R and RStudio
4
Conclusion to Section 1
      Section 2 : Reading in Data from Different Sources
5
Read in CSV & Excel Data Preview
6
Read in Data from Online CSV
7
Read in Zipped File
8
Read Data from a Database
9
Read in JSON Data
10
Read in Data from PDF Documents
11
Read in Tables from PDF Documents
12
Conclusion to Section 2
      Section 3 : Webscraping: Extract Data from Webpages
13
Read in Data From Online Google Sheets Preview
14
Read in Data from Online HTML Tables-Part 1
15
Read in Data from Online HTML Tables-Part 2
16
Get and Clean Data from HTML Tables
17
Read Text Data from an HTML Page
18
Introduction to Selector Gadget
19
More Webscraping With rvest-IMDB Webpage
20
Another Way of Accessing Webpage Elements
21
Conclusions to Section 3
      Section 4 : Introduction to APIs
22
What is an API? Preview
23
Extract Text Data from Guardian Newspaper
      Section 5 : Text Data Mining from Social Media
24
Extract Data from Facebook
25
Get More out Of Facebook
26
Set up a Twitter App for Mining Data from Twitter
27
Extract Tweets Using R Preview
28
More Twitter Data Extraction Using R
29
Get Tweet Locations
30
Get Location Specific Trends
31
Learn More About the Followers of a Twitter Handle
32
Another Way of Extracting Information From Twitter- the rtweet Package
33
Geolocation Specific Tweets With "rtweet"
34
More Data Extraction Using rtweet
35
Locations of Tweets
36
Mining Github Using R Preview
37
Set up the FourSquare App
38
Extract Reviews for Venues on FourSquare
39
Conclusions to Section 5
      Section 6 : Exploring Text Data For Preliminary Ideas
40
Explore Tweet Data Preview
41
A Brief Explanation
42
EDA With Text Data
43
Examine Multiple Document Corpus of Text
44
Brief Introduction to tidytext
45
Text Exploration & Visualization with tidytext
46
Explore Multiple Texts with tidytext Preview
47
Count Unique Words in Tweets
48
Visualizing Text Data as TF-IDF
49
TF-IDF in Graphical Form
50
Conclusions to Section 6
      Section 7 : Natural Language Processing: Sentiment Analysis
51
Wordclouds for Visualizing Tweet Sentiments: India's Demonetization Policy
52
Wordclouds for Visualizing Reviews
53
Tidy Wordclouds
54
Quanteda Wordcloud
55
Word Frequency in Text Data
56
Tweet Sentiments- Mugabe's Ouster
57
Tidy Sentiments- Sentiment Analysis Using tidytext
58
Examine the Polarity of Text
59
Examine the Polarity of Tweets
60
Topic Modelling a Document
61
Topic Modelling Multiple Documents
62
Topic Modelling Tweets Using Quanteda
63
Conclusions to Section 7
      Section 8 : Text Data and Machine Learning
64
Clustering for Text Data
65
Clustering Tweets with Quanteda
66
Regression on Text Data
67
Identify Spam Emails with Supervised Classification
68
Introduction to RTextTools
69
More on RTextTools
70
The Doc2Vec Approach
71
Doc2Vec Approach For Predicting a Binary Outcome
72
Doc2Vec Approach for Multi-class Classification
      Section 9 : Network Analysis
73
A Small (Social) Network
74
A More Theoretical Explanation
75
Build & Visualize a Network
76
Network of Emails
77
More on Network Visualization
78
Analysis of Tweet Network
79
Identify Word Pair Networks
80
Network of Words

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