Learning Application of R: Part 1 of 3 (Emotion Analysis)
5.0 (2 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
16 students enrolled

Learning Application of R: Part 1 of 3 (Emotion Analysis)

In this Course Series, we discuss Practical Applications developed using R. In this part, we discuss Emotion Analysis.
5.0 (2 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
16 students enrolled
Created by Partha Majumdar
Last updated 3/2020
English
Current price: $83.99 Original price: $119.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 3.5 hours on-demand video
  • 25 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • Sentiment Analysis
  • Emotion Analysis
  • Creating Shiny App
  • Creating R Markdown
  • Use of many related R Libraries
Course content
Expand all 20 lectures 03:43:11
+ Creating a Simple Web Application using Shiny - Example 1
3 lectures 51:02

In this video, we discuss how to convert numbers to words using a R Program. We discuss code for 2 conventions - European and Indian.

Preview 18:20

R is powerful Language to implement algorithms for Data Sciences, Machine Learning, Artificial Intelligence, etc.

One need for every Develop is to develop a Data Product around the algorithm developed so that public in general could use the algorithm and benefit from it.

One easy and powerful way to develop Web Application in R is to develop SHINY Apps.

SHINY Apps can be very complex and create very complex Web Applications. However, one needs to make a start with a simple application. Once one understand the basics, developing any application becomes possible.

This video goes through the nuances of creating a simple SHINY App.

Creating Shiny App for Number to Words Application
31:04

This is an exercise for the Learners to write a Number ot Words conversion program for any language of their choice.

Preview 01:38
+ Creating a Simple Web Application using Shiny - Example 2
5 lectures 01:18:06

In this video, we discuss how to create a Word Cloud. We create Word Cloud from Structured Data and from Unstructured Data.

Word Cloud
12:17

R Markdown allows for creating Dynamic Documents. R Markdown can take any plain text and juxtapose with R Code.

This video provides details of how to create R Markdown. Also, it provides details of some of the possibilities with R Markdown.. Lastly, the video demonstrates how to produce dynamic documents from R Markdown using RStudio.

Creating a R Markdown
19:35

In this video, we explore many more aspects of Shiny Programming through the example of creating a Shiny App for showing Word Cloud for any File - Text or PDF - provided as input.

The video became a bit too large in terms of file size and is thus broken into 2 parts.

Creating Shiny App for Word Cloud - Part 1
20:14

This is the second and concluding part of the discussion on creating a Shiny App for display Word Cloud from any input file - Text File or PDF File - provided by the user.

Creating Shiny App for Word Cloud - Part 2
23:23

This is an exercise for the Learners to create a Word Cloud from text read from a Word Document.

Preview 02:37
+ Sentiment Analysis
3 lectures 36:23

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Sentiment Analysis Discussion
17:51

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Sentiment Analysis Demonstration
17:17

This is a coding exercise for the Learners to develop a SHINY App using which Users can conduct Sentiment Analysis.

Preview 01:15
+ Emotion Analysis
6 lectures 45:34

From this video, we start our discussion on Emotion Analysis. We will walk through the code needed to conduct this analysis. During the code walkthrough, I will try to explain to the conceptual and technical nuances involved.

Due to restrictions on the size of the video files, I have broken up the discussion into multiple videos.

Emotion Analysis Discussion - Part 1
10:37

In the second part of our discussion on Emotion Analysis, we discuss 2 main topics:

1. How to extract the prevalent emotion in the document. We do this by checking the contributions of the sentiments by each word used in different contexts across the document,

2. What the flow of Sentiments across the document. We determine where in the document Positive and Negative Sentiments have been used in the document.

Emotion Analysis Discussion - Part 2
09:54

We continue our discussion on Emotion Analysis in this video. In this part, we establish the most important aspect of "what is the prevalent emotion extracted from the Input Document".

Emotion Analysis Discussion - Part 3
06:06

We continue our discussion on Emotion Analysis in this video. In this video, we try to establish which words in a document espouse what Emotions and to what degree.

Emotion Analysis Discussion - Part 4
05:54

In this video, I demonstrate a Data Product created for Emotion Analysis.

Preview 10:01

This is a Researching and Coding Exercise for the Learners to create their own Emption Analysis Model.

Exercise 4: Create your own Emotion Analysis Model
03:02
Requirements
  • Knowledge of R Programming Essential
  • Knowledge of using RStudio Essential
  • Exposure to Computer Software Programming Essential
Description

In this course series, application developed using R language are discussed. We will cover topics from simple applications like creating Word Clouds to complex applications like Emotion Analysis, Churn Management, Stock Market Prediction, etc. All the applications discussed in the course have been developed through research by me. All the products discussed are being used for different organisational operations in my company and/or by our Customers.

The course start with discussing simple applications through which concepts and applications of supporting tools including R Language features. Tools like knitR, Shiny, etc are discussed. The goal is to develop a concept into an Algorithm and end with developing a Data Product which could be released for consumption in the Market.  We will discuss how to publish the Data Products so that it could be available to everyone on the Internet.

Though this course provides a primer on R Programming, it is highly desirable that the Learner has a reasonably good exposure to R Programming. The learner is expected to have installed tools using which R Programming could be conducted. In case, the Learner needs help with getting started with R Programming, they could go through the course "Learning R through an Example".

The course is broken into 3 parts. In each part,1 or more main application is discussed.

In this part, i.e. Part 1, Emotion Analysis is the topic of discussion.

The discussion on Emotion Analysis starts with understanding Sentiment Analysis and then proceeds to Emotion Analysis. The needed concepts of Sentiment Analysis and Emotion Analysis are discussed. The different libraries available in R Language is exposed. The course ends with developing a Data Product for Emotion Analysis.

Who this course is for:
  • Marketing Specialist
  • Business Development Specialist
  • Business Intelligence Consultant
  • Business Analyst
  • E-Commerce Specialist
  • Salesperson
  • HR Specialist
  • Researcher
  • Student