Learn Emotion Analysis With R

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Learn Emotion Analysis With R

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Author : Partha Majumdar
language : en
Publisher: BPB Publications
Release Date : 2021-06-02

Learn Emotion Analysis With R written by Partha Majumdar and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-02 with Computers categories.

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. ● Understanding the essentials of R programming for developing systems for Emotion Analysis. 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. TABLE OF CONTENTS Section 1 Introduction to R Programming 1 Getting Started with R 2 Simple Operations using R 3 Developing Simple Applications in R Section 2 Introduction to Shiny Programming 4 Structure of Shiny Applications 5 Shiny Application 1 6 Shiny Application 2 Section 3 Emotion Analysis 7 Sentiment Analysis 8 Emotion Analysis 9 ZEUSg Section 4 Twitter Data Analysis 10 Introduction to Twitter Data Analysis 11 Emotion Analysis on Twitter Data 12 Chidiya BONUS CHAPTER WhatsApp Chat Analysis

Learning Social Media Analytics With R

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Author : Raghav Bali
language : en
Publisher:
Release Date : 2017-05-26

Learning Social Media Analytics With R written by Raghav Bali and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-26 with Computers categories.

Tap into the realm of social media and unleash the power of analytics for data-driven insights using RAbout This Book* A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data* Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms.* Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering.Who This Book Is ForIt is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise.What You Will Learn* Learn how to tap into data from diverse social media platforms using the R ecosystem* Use social media data to formulate and solve real-world problems* Analyze user social networks and communities using concepts from graph theory and network analysis* Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels* Understand the art of representing actionable insights with effective visualizations* Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on* Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many moreIn DetailThe Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data.The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.Style and approachThis book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

Social Media Mining With R

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Author : Nathan Danneman
language : en
Publisher:
Release Date : 2014

Social Media Mining With R written by Nathan Danneman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.

Social Media Mining With R

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Author : Richard Heimann
language : en
Publisher: Packt Pub Limited
Release Date : 2014

Social Media Mining With R written by Richard Heimann and has been published by Packt Pub Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computers categories.

A concise, handson guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world.Whether you are an undergraduate who wishes to get handson experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

Conducting Sentiment Analysis

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Author : Lei Lei
language : en
Publisher: Cambridge University Press
Release Date : 2021-09-23

Conducting Sentiment Analysis written by Lei Lei and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Language Arts & Disciplines categories.

This Element provides a basic introduction to sentiment analysis, aimed at helping students and professionals in corpus linguistics to understand what sentiment analysis is, how it is conducted, and where it can be applied. It begins with a definition of sentiment analysis and a discussion of the domains where sentiment analysis is conducted and used the most. Then, it introduces two main methods that are commonly used in sentiment analysis known as supervised machine-learning and unsupervised learning (or lexicon-based) methods, followed by a step-by-step explanation of how to perform sentiment analysis with R. The Element then provides two detailed examples or cases of sentiment and emotion analysis, with one using an unsupervised method and the other using a supervised learning method.

People Analytics Text Mining With R

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Author : Mong Shen Ng
language : en
Publisher: Independently Published
Release Date : 2019-03-21

People Analytics Text Mining With R written by Mong Shen Ng and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-21 with Business & Economics categories.

You don't need to buy expensive statistical software like SPSS. This book teaches you R (R can be downloaded for free), People Analytics, Social Media Analytics, Text Mining and Sentiment Analysis. It is written for people with absolutely NO knowledge of R programming, with step-by-step print-screen instructions. The sample R codes are kept simple & short so that you are not overwhelmed with too much unnecessary information, and focuses on teaching you the R codes relevant to people analytics, so that you'll be up-and-running in no time. If you are new to R programming, this is the book for you. As R is developed specially for statistical analysis, you can run complicated statistical number crunching (Correlation, Multiple & Logistic Regression, etc.) by simply entering a few commands. This book covers the full People Analytics scope (Benefits, Compensation, Culture, Diversity & Inclusion, Engagement, Leadership, Learning & Development, Personality Traits, Performance Management, Recruitment, Sales Incentives) with numerous real-world examples, and shows how R programming can help you: 1) Run Social Media Analytics, Text mining & Sentiment Analysis with R. 2) Predict employees' flight-risk using R's Correlation & Logistic Regression function. 3) Identify the personality traits of top performing Customer Service staff and Sales staff using R's correlation function. 4) Predict impact of Employee Engagement on Customer Satisfaction, Revenue and Shareholder Returns, etc. using R's Correlation & Multiple Regression function. 5) Predict impact of Learning & Development on Sales, using R's Multiple Regression function. 6) Predict Diversity & Inclusion's impact on Revenue and EBIT using R's Multiple Regression function.

Text Mining With R

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Author : Julia Silge
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-12

Text Mining With R written by Julia Silge and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-12 with Computers categories.

Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.

R Data Analysis Projects

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Author : Gopi Subramanian
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-17

R Data Analysis Projects written by Gopi Subramanian and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-17 with Computers categories.

Get valuable insights from your data by building data analysis systems from scratch with R. About This Book A handy guide to take your understanding of data analysis with R to the next level Real-world projects that focus on problems in finance, network analysis, social media, and more From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end-to-end data analysis pipelines using R Who This Book Is For If you are looking for a book that takes you all the way through the practical application of advanced and effective analytics methodologies in R, then this is the book for you. A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this book. What You Will Learn Build end-to-end predictive analytics systems in R Build an experimental design to gather your own data and conduct analysis Build a recommender system from scratch using different approaches Use and leverage RShiny to build reactive programming applications Build systems for varied domains including market research, network analysis, social media analysis, and more Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively Communicate modeling results using Shiny Dashboards Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling In Detail R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it's one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You'll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You'll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You'll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you'll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this book, you'll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle. Style and approach This book takes a unique, learn-as-you-do approach, as you build on your understanding of data analysis progressively with each project. This book is designed in a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently.

R Data Analysis And Visualization

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Author : Tony Fischetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-06-24

R Data Analysis And Visualization written by Tony Fischetti and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-24 with Computers categories.

Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will Learn Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Handle missing data gracefully using multiple imputation Create diverse types of bar charts using the default R functions Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on Understand relationships between market factors and their impact on your portfolio Harness the power of R to build machine learning algorithms with real-world data science applications Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.

Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks

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Author : Arindam Chaudhuri
language : en
Publisher: Springer
Release Date : 2019-04-06

Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks written by Arindam Chaudhuri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-06 with Computers categories.

This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.