Deep Learning For Social Media Data Analytics

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Deep Learning For Social Media Data Analytics

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Author : Tzung-Pei Hong
language : en
Publisher: Springer Nature
Release Date : 2022-09-18

Deep Learning For Social Media Data Analytics written by Tzung-Pei Hong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-18 with Computers categories.

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics.

Deep Learning In Data Analytics

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Author : Debi Prasanna Acharjya
language : en
Publisher: Springer Nature
Release Date : 2021-08-11

Deep Learning In Data Analytics written by Debi Prasanna Acharjya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-11 with Technology & Engineering categories.

This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.

Deep Learning For Social Media Data Analytics

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Author : Tzung-Pei Hong
language : en
Publisher:
Release Date : 2022

Deep Learning For Social Media Data Analytics written by Tzung-Pei Hong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics. .

Social Media For Civic Education

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Author : Amy L. Chapman
language : en
Publisher:
Release Date : 2022

Social Media For Civic Education written by Amy L. Chapman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Big data categories.

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics. .

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.

Principles Of Social Networking

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Author : Anupam Biswas
language : en
Publisher: Springer Nature
Release Date : 2021-08-18

Principles Of Social Networking written by Anupam Biswas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-18 with Technology & Engineering categories.

This book presents new and innovative current discoveries in social networking which contribute enough knowledge to the research community. The book includes chapters presenting research advances in social network analysis and issues emerged with diverse social media data. The book also presents applications of the theoretical algorithms and network models to analyze real-world large-scale social networks and the data emanating from them as well as characterize the topology and behavior of these networks. Furthermore, the book covers extremely debated topics, surveys, future trends, issues, and challenges.

Artificial Intelligence Trends For Data Analytics Using Machine Learning And Deep Learning Approaches

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Author : K. Gayathri Devi
language : en
Publisher:
Release Date : 2024-10-04

Artificial Intelligence Trends For Data Analytics Using Machine Learning And Deep Learning Approaches written by K. Gayathri Devi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-04 with Computers categories.

This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.

Social Media Data Mining And Analytics

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Author : Gabor Szabo
language : en
Publisher: John Wiley & Sons
Release Date : 2018-10-23

Social Media Data Mining And Analytics written by Gabor Szabo and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-23 with Computers categories.

Harness the power of social media to predict customer behavior and improve sales Social media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Social Media Data Mining and Analytics shows analysts how to use sophisticated techniques to mine social media data, obtaining the information they need to generate amazing results for their businesses. Social Media Data Mining and Analytics isn't just another book on the business case for social media. Rather, this book provides hands-on examples for applying state-of-the-art tools and technologies to mine social media - examples include Twitter, Wikipedia, Stack Exchange, LiveJournal, movie reviews, and other rich data sources. In it, you will learn: The four key characteristics of online services-users, social networks, actions, and content The full data discovery lifecycle-data extraction, storage, analysis, and visualization How to work with code and extract data to create solutions How to use Big Data to make accurate customer predictions How to personalize the social media experience using machine learning Using the techniques the authors detail will provide organizations the competitive advantage they need to harness the rich data available from social media platforms.

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.

Deep Learning For Data Analytics

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Author : Himansu Das
language : en
Publisher: Academic Press
Release Date : 2020-05-31

Deep Learning For Data Analytics written by Himansu Das and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-31 with Science categories.

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.