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- Author: Stan Matwin
- Publisher: Springer
- ISBN-10: 303133616X
- ISBN-13: 9783031336164
- Format: 15.6 x 23.4 x 0.5 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
This book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks.
The book is divided into five chapters and provides an extensive bibliography consisting of more than 250 papers. After a quick introduction and survey of the book in the first chapter, chapter 2 is devoted to the discussion of data models and ontologies for social network analysis. Next, chapter 3 deals with text generation and generative text models and the dangers they pose to social media and society at large. Chapter 4 then focuses on topic modelling and sentiment analysis in the context of social networks. Finally, Chapter 5 presents graph theory tools and approaches to mine and model social networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified.
The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications.
EXTRA 10 % discount with code: EXTRA
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- Author: Stan Matwin
- Publisher: Springer
- ISBN-10: 303133616X
- ISBN-13: 9783031336164
- Format: 15.6 x 23.4 x 0.5 cm, softcover
- Language: English English
This book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks.
The book is divided into five chapters and provides an extensive bibliography consisting of more than 250 papers. After a quick introduction and survey of the book in the first chapter, chapter 2 is devoted to the discussion of data models and ontologies for social network analysis. Next, chapter 3 deals with text generation and generative text models and the dangers they pose to social media and society at large. Chapter 4 then focuses on topic modelling and sentiment analysis in the context of social networks. Finally, Chapter 5 presents graph theory tools and approaches to mine and model social networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified.
The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications.
Reviews