Learn Emotion Analysis with R: Perform Sentiment Assessments, Extract Emotions, and Learn NLP Techniques Using R and Shiny

by Partha Majumdar

Length: 552 pages
Edition: 1
Language: English
Publisher: BPB Publications
Publication Date: 2021-06-02
ISBN-10: 9390684153
ISBN-13: 9789390684151
Sales Rank: #5394362 (See Top 100 Books)

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Learn to assess textual data and extract sentiments using various text analysis R packages

Key Features

In-depth coverage on core principles, challenges, and application of Emotion Analysis.
Includes real-world examples to simplify practical uses of R, Shiny, and various popular NLP techniques.
Covers different strategies used in Sentiment and Emotion Analysis.

Description

This book covers how to conduct Emotion Analysis based on Lexicons. Through a detailed code walkthrough, the book will explain how to develop systems for Sentiment and Emotion Analysis from popular sources of data, including WhatsApp, Twitter, etc.

The book starts with a discussion on R programming and Shiny programming as these will lay the foundation for the system to be developed for Emotion Analysis. Then, the book discusses essentials of Sentiment Analysis and Emotion Analysis. The book then proceeds to build Shiny applications for Emotion Analysis. The book rounds off with creating a tool for Emotion Analysis from the data obtained from Twitter and WhatsApp.

Emotion Analysis can be also performed using Machine Learning. However, this requires labeled data. This is a logical next step after reading this book.

What you will learn

Learn the essentials of Sentiment Analysis.
Learn the essentials of Emotion Analysis.
Conducting Emotion Analysis using Lexicons.
Learn to develop Shiny applications.

Who this book is for

This book aspires to teach NLP users, ML engineers, and AI engineers who want to develop a strong understanding of Emotion and Sentiment Analysis. No prior knowledge of R programming is needed. All you need is just an open mind to learn and explore this concept.

Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgements
Preface
Errata
Table of Contents
Section - 1
    1. Getting Started with R
        Structure
        Objectives
        Brief introduction to R language
        Setting up the R software
        Obtaining the R software
        Installing R
        Invoking R
        Setting up RStudio
        Obtaining the RStudio software
        Installing RStudio
        Invoking RStudio
        Introduction to packages
        Installing packages
        Installing packages using RStudio GUI
        Installing packages using CLI
        Loading libraries
        Introduction to vector
        Assigning vectors to variables
        Checking the type of a vectors
        Checking the length of a vectors
        Conducting statistical operations on a vectors
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    2. Simple Operations Using R
        Structure
        Objectives
        Introduction to data frame
        Creating an empty data frame
        Creating a Data Frame from vector(s)
        Renaming column(s) in a data frame
        Referencing row(s)/column(s) of a data frame
        Referencing a data frame based on conditions
        Adding column(s) to a data frame
        Adding row(s) to a data frame
        Sorting a data frame
        Visualizing data in a data frame
        Histogram
        Box and Whisker chart
        Pie chart
            Scatter plots
        Reading data from a CSV file
        Reading data from a database
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    3. Developing Simple Applications in R
        Structure
        Objectives
        Programming conditions and loops in R
        Functions in R
        Application: number to words converter
        Writing our own function
            trim() function
            helper() function
            convert2DigitNumbers() function
            convert3DigitNumbers() function
            convertThousands() function
            convertLakhs() function
            convertCrores() function
            number2wordsIndia() function
        R library for number to words conversion
            Application: Word Cloud
        Creating a Word Cloud from structured data
        Creating a Word Cloud from unstructured data
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
Section - 2
    4. Structure of a Shiny Application
        Structure
        Objectives
        Creating a Shiny application using RStudio
        Structure of a Shiny application
        Client-side program
        Displaying title
            The body of the web page
        Taking input
        Displaying output
        Server-side program
        Running the application
        Creating Separate Files for Client-side and Server-side Programs
            ui.R
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    5. Shiny Application 1
        Structure
        Objectives
        Designing the user interface
        The UI for SankhyaSaabdh
        Coding the user interface
        Displaying images in Shiny applications
        Displaying text in Shiny applications
        Accepting number only input in Shiny applications
        Displaying the text output in Shiny applications
        Creating layouts in Shiny applications
        Coding the server side of SankhyaSaabdh
        The complete code of SankhyaSaabdh
        Publishing a Shiny application
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    6. Shiny Application 2
        Structure
        Objectives
        Creating RMarkdown document
        Setting up the YAML header
        Creating the body of the RMarkdown
        Embedding R code in RMarkdown
        Writing our First RMarkdown
        Generating Output from RMarkdown
        Developing Shiny application ShabdhMegh
        What We will develop?
        Creating tabs in Shiny applications
        Creating radio button in Shiny applications
        Inputting TEXT files in Shiny applications
            Server-side Code side code for inputting text file
        Inputting PDF files in Shiny applications
        Server-side Code for inputting PDF Files
        Switching Modes between TEXT file and PDF file
        What we do before and after a file is input?
        Creating output when an input file is provided
        Computing the word frequency
        Creating the data table output
        Displaying the Word Cloud
            Displaying the progress bar
            Resetting output when a new input file is provided
        Programming the download button
            Programming the client-side of the Shiny application
        Programming the server-side of the Shiny application
            Programming the RMarkdown
        Complete code of ShabdhMegh
            app.R
            ShabdhMegh.RMD
        Conclusion
        Points to Remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
Section - 3
    7. Sentiment Analysis
        Structure
        Objectives
        What is sentiment analysis?
        Approaches to sentiment analysis
            Knowledge-based approach
            Statistical approach
            Hybrid approach
        Sentiment knowledge bases in R
            afinn Lexicon
            bing Lexicon
            loughran Lexicon
            nrc Lexicon
        Conducting sentiment analysis
        Extracting words from the text
        Generating the words frequency
        Finding sentiment across the text
            Determine the sentiment of each word
        Determine the sentiment of each line
            Separate the counts of positive and negative sentiments
        Calculate the net sentiments score for each chunk
            Plot the net sentiments score
        Determining the contribution of each word in the sentiment score
        Visualizing words with positive and negative sentiment
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    8. Emotion Analysis
        Structure
        Objectives
        Conducting emotion analysis
        Reading and cleaning data
        Removing stop words
        Word analysis
        Check diversity of word frequencies
            Most frequently used words in the text
            Emotions expressed by the words – visualization 1
            Emotions expressed by the words – visualization 2
            Emotions expressed by the words – visualization 3
        Sentiment flow in the text
            Finding the most prevalent emotion in the text
            Further analysis of the document
        Bigram analysis
        Trigram analysis
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    9. ZEUSg
        Structure
        Objectives
        Design of ZEUSg
            Landing page
        Outputs from ZEUSg
        Programming ZEUSg
            Giving credit for using a library
            Libraries used to develop ZEUSg
            Creating tabs within a tab
            Reading the input file
        The complete code of ZEUSg
        app.R
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
Section - 4
    10. Introduction to Twitter Data Analysis
        Structure
        Objectives
        Introduction to Twitter
        Twitter Developer Account
            URL for creating a Twitter Developer Account
        Creating a Twitter Developer Account
            Steps for creating an app
            App approval by Twitter
        Google Maps API key
        URL for creating a Google Maps API key
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    11. Emotion Analysis on Twitter Data
        Structure
        Objectives
        A Few Features of Twitter
        Fetching data from Twitter
        Library required for fetching tweets
            Setting up a Twitter account
        Authenticating the Twitter account
        Fetching tweets by search string(s)
        Fetching tweets for a Location
        Displaying tweets using DT
        Displaying tweets with their links
        Analyzing information obtained from Twitter
            Time Series chart for when tweets were posted
        Country analysis
        Place analysis
        Language analysis
        User analysis
        Source analysis
        Hashtag analysis
        Location analysis with maps
        Interactive maps using a leaflet
        Emotion analysis of tweets
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    12. Chidiya
        Structure
        Objectives
        Features of Chidiya
            Landing page
        Global code
            Loading the required libraries
        Global functions
        Generic function to display a message box
            Function to fetch the subjects of tweets stored in the Chidiya database
        Function to fetch the Previously Searched Subjects
        Function to read the initial set of tweets
        Function to extract words from tweets
        Function to extract emotions from tweets
        Function to save statistics
        Initialization code
        Reading the system parameters
        Setting up the Twitter Account
        Setting up the database
        Setting up the Google Maps API
        Setting up the Lexicon
        Setting up the data for initial display
        Server-side code
        Initiating response to user input
        Fetching the tweets
        Making Chidiya responsive
        Client-side code
        The complete for code Chidiya
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
    Bonus-WhatsApp Chat Analysis
        Structure
        Objectives
        Introduction to WhatsApp
        How to download data from WhatsApp?
        Downloading WhatsApp data from Apple iPhones
        Downloading WhatsApp data from Android phones
        The structure of WhatsApp chat data
        Reading WhatsApp chat data using R
        Visualizing WhatsApp chat data
        Messages per day
        Messages per weekday
        Radar chart
        Messages per hour
        Heat map
        Messages per author
        Emoji analysis
        Word analysis
        Conclusion
        Points to remember
        Multiple choice questions
            Answers to MCQs
        Questions
        Key terms
Index