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: Packt Publishing Ltd
Release Date : 2017-05-26
Learning Social Media Analytics With R written by Raghav Bali 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 2017-05-26 with Computers categories.
Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About 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 For It 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 more In Detail The 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 approach This 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.
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.
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages
Deep Learning Based Approaches For Sentiment Analysis
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Author : Basant Agarwal
language : en
Publisher: Springer Nature
Release Date : 2020-01-24
Deep Learning Based Approaches For Sentiment Analysis written by Basant Agarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-24 with Technology & Engineering categories.
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Machine And Deep Learning Techniques For Emotion Detection
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Author : Rai, Mritunjay
language : en
Publisher: IGI Global
Release Date : 2024-05-14
Machine And Deep Learning Techniques For Emotion Detection written by Rai, Mritunjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-14 with Psychology categories.
Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies.
Fundamentals Of Supervised Machine Learning
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Author : Giovanni Cerulli
language : en
Publisher: Springer Nature
Release Date : 2023-11-14
Fundamentals Of Supervised Machine Learning written by Giovanni Cerulli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-14 with Computers categories.
This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over different software platforms. After introducing the machine learning basics, the focus turns to a broad spectrum of topics: model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part, where the basics of the methodologies are explained, followed by an applicative part, where the methods are applied to real-world datasets. Numerous examples are included and, for ease of reproducibility, the Python, R, and Stata codes used in the text, along with the related datasets, are available online. The intended audience is PhD students, researchers and practitioners from various disciplines, including economics and other social sciences, medicine and epidemiology, who have a good understanding of basic statistics and a working knowledge of statistical software, and who want to apply machine learning methods in their work.
Multi Strategy Learning Environment
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Author : Vrince Vimal
language : en
Publisher: Springer Nature
Release Date : 2024-05-28
Multi Strategy Learning Environment written by Vrince Vimal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Computers categories.
The book presents selected papers from International Conference on Multi-Strategy Learning Environment (ICMSLE 2024), held at Graphic Era Hill University, Dehradun, India, during 12–13 January 2024. This book presents current research in machine learning techniques, deep learning theories and practices, interpretability and explainability of AI algorithms, game theory and learning, multi-strategy learning (MSL) in distributed and streaming environments, and adaptive data analysis and selective inference.
Advances In Information Communication Technology And Computing
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Author : Vishal Goar
language : en
Publisher: Springer Nature
Release Date : 2023-05-29
Advances In Information Communication Technology And Computing written by Vishal Goar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-29 with Technology & Engineering categories.
The book is a collection of best selected research papers presented at the International Conference on Advances in Information Communication Technology and Computing (AICTC 2022), held in Government Engineering College Bikaner, Bikaner, India during 17 – 18 December 2022. The book covers ICT-based approaches in the areas of ICT for Energy Efficiency, Life Cycle Assessment of ICT, Green IT, Green Information Systems, Environmental Informatics, Energy Informatics, Sustainable HCI, or Computational Sustainability.
Call For Widening Participation Short Papers From Eurocall 2020
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Author : Karen-Margrete Frederiksen
language : en
Publisher: Research-publishing.net
Release Date : 2020-12-14
Call For Widening Participation Short Papers From Eurocall 2020 written by Karen-Margrete Frederiksen and has been published by Research-publishing.net this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-14 with Foreign Language Study categories.
Despite the Covid-19 pandemic, the EUROCALL society succeeded in holding the 28th EUROCALL conference, EUROCALL2020, on 20-21 August as an online, two-day gathering. The transition process required to make this happen was demanding and insightful for everyone involved, and, in many ways, a logical consequence of the core content and purpose of EUROCALL. Who would be better suited to transform an onsite conference into an online event than EUROCALL? CALL for widening participation was this year’s theme. We welcomed contributions from both theoretical and practical perspectives in relation to the many forms and contexts of CALL. We particularly welcomed longitudinal studies or studies that revisited earlier studies. The academic committee accepted 300 abstracts for paper presentations, symposia, workshops, and posters under this theme; 57 short papers are published in this volume. We hope you will enjoy reading this volume, the first one to reflect a one hundred percent online EUROCALL conference/Online Gathering.
Proceedings Of Third Doctoral Symposium On Computational Intelligence
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Author : Ashish Khanna
language : en
Publisher: Springer Nature
Release Date : 2022-11-09
Proceedings Of Third Doctoral Symposium On Computational Intelligence written by Ashish Khanna and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-09 with Technology & Engineering categories.
This book features high-quality research papers presented at Third Doctoral Symposium on Computational Intelligence (DoSCI 2022), organized by Institute of Engineering and Technology (IET), AKTU, Lucknow, India, on March 5, 2022. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high performance computing, biomedical computing, and decision support and decision making.