Big Data Analytics In Cognitive Social Media And Literary Texts

This book provides a comprehensive overview of the theory and praxis of Big Data Analytics and how these are used to extract cognition-related information from social media and literary texts. It presents analytics that transcends the borders of discipline-specific academic research and focuses on knowledge extraction, prediction, and decision-making in the context of individual, social, and national development. The content is divided into three main sections: the first of which discusses various approaches associated with Big Data Analytics, while the second addresses the security and privacy of big data in social media, and the last focuses on the literary text as the literary data in Big Data Analytics. Sharing valuable insights into the etiology behind human cognition and its reflection in social media and literary texts, the book benefits all those interested in analytics that can be applied to literature, history, philosophy, linguistics, literary theory, media & communication studies and computational/digital humanities.

Opinion mining and text analytics are used widely across numerous disciplines and fields in today’s society to provide insight into people’s thoughts, feelings, and stances. This data is incredibly valuable and can be utilized for a range of purposes. As such, an in-depth look into how opinion mining and text analytics correlate with social media and literature is necessary to better understand audiences. The Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media introduces the use of artificial intelligence and big data analytics applied to opinion mining and text analytics on literary works and social media. It also focuses on theories, methods, and approaches in which data analysis techniques can be used to analyze data to provide a meaningful pattern. Covering a wide range of topics such as sentiment analysis and stance detection, this publication is ideal for lecturers, researchers, academicians, practitioners, and students.

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.

Artificial intelligence has been utilized in a diverse range of industries as more people and businesses discover its many uses and applications. A current field of study that requires more attention, as there is much opportunity for improvement, is the use of artificial intelligence within literary works and social media analysis. The Handbook of Research on Artificial Intelligence Applications in Literary Works and Social Media presents contemporary developments in the adoption of artificial intelligence in textual analysis of literary works and social media and introduces current approaches, techniques, and practices in data science that are implemented to scrap and analyze text data. This book initiates a new multidisciplinary field that is the combination of artificial intelligence, data science, social science, literature, and social media study. Covering key topics such as opinion mining, sentiment analysis, and machine learning, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

This book covers computationally innovative methods and technologies including data collection and elicitation, data processing, data analysis, data visualizations, and data presentation. It explores how digital humanists have harnessed the hypersociality and social technologies, benefited from the open-source sharing not only of data but of code, and made technological capabilities a critical part of humanities work. Chapters are written by researchers from around the world, bringing perspectives from diverse fields and subject areas. The respective authors describe their work, their research, and their learning. Topics include semantic web for cultural heritage valorization, machine learning for parody detection by classification, psychological text analysis, crowdsourcing imagery coding in natural disasters, and creating inheritable digital codebooks.Designed for researchers and academics, this book is suitable for those interested in methodologies and analytics that can be applied in literature, history, philosophy, linguistics, and related disciplines. Professionals such as librarians, archivists, and historians will also find the content informative and instructive.

"This book uses artificial intelligence and big data analytics to conduct opinion mining and text analytics on literary works and social media, focusing on theories, method, applications and approaches of data analytic techniques that can be used to extract and analyze data from literary books and social media, in a meaningful pattern"--

This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on concepts, techniques and methods for organizing, curating, processing, analyzing, and visualizing big social data: from text to image and video analytics. It provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery. The book covers a large body of knowledge that will help practitioners and researchers in understanding the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics. It also provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry. It provides a deep knowledge in social data analytics by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics.