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Record Nr. |
UNINA9910734827203321 |
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Autore |
Matwin Stan |
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Titolo |
Generative Methods for Social Media Analysis / / by Stan Matwin, Aristides Milios, Paweł Prałat, Amilcar Soares, François Théberge |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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3031336178 |
9783031336171 |
9783031336164 |
303133616X |
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Edizione |
[First edition.] |
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Descrizione fisica |
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Collana |
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SpringerBriefs in Computer Science, , 2191-5776 |
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Disciplina |
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Soggetti |
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Quantitative research |
Artificial intelligence |
Natural language processing (Computer science) |
Social media |
Data Analysis and Big Data |
Artificial Intelligence |
Natural Language Processing (NLP) |
Social Media |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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1. Introduction -- 2. Ontologies and Data Models for Cross-platform Social Media Data -- 3. Methods for Text Generation in NLP -- 4. Topic and Sentiment Modelling for Social Media -- 5. Mining and Modelling Complex Networks -- 6. Conclusions. |
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Sommario/riassunto |
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"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 |
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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." -- Publisher's description. |
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