Authors:
- Provides a language-agnostic method for social media text analysis, which is not based on a specific grammar, semantics or machine learning techniques
- Detects topics from large text documents and extracts the main opinion without any human intervention
- Compares a variety of techniques and provides a smooth transition from theory to practice with multiple experiments and results
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
This book provides a novel method for topic detection and classification in social networks. The book addresses several research and technical challenges that are currently being investigated by the research community, from the analysis of relations and communications between members of a community, to quality, authority, relevance and timeliness of the content, traffic prediction based on media consumption, spam detection, to security, privacy and protection of personal information. Furthermore, the book discusses innovative techniques to address those challenges and provides novel solutions based on information theory, sequence analysis and combinatorics, which are applied on real data obtained from Twitter.
Keywords
- Dynamic Social Networks
- Joint Sequence Complexity
- Joint Complexity
- Analytic Combinatorics
- Analytic Combinatorics Application in Social Networks
- Compressive Sensing
- Sparse Representation
- Kalman Filter
- Privacy in Social Networks
- Topic Detection
- Topic Detection in Social Networks
- Classification in Social Networks
- Automatic Classification of Topics
- Trend Sensing
- Analysis in Twitter
Authors and Affiliations
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Massachusetts Institute of Technology , Cambridge, USA
Dimitrios Milioris
About the author
Dr. Dimitrios Milioris is a research associate and lecturer at the Massachusetts Institute of Technology (MIT). He received his Ph.D. from École Polytechnique Paris (2015, honors) while a scholar at Columbia University, New York, USA, as an Alliance Program awardee (2013 – 2014). He received his double M.Sc. degree (2011, first in class, honors) in computer science & applied mathematics from Paris XI University and the École Polytechnique, and his B.Sc. degree (2009, honors) in computer science from the University of Crete, Greece. Prior to joining MIT, he was a researcher at Bell Labs, Alcatel-Lucent in Paris, France, and a member of the Mathematics of Dynamic & Complex Networks Department. Prior to joining Bell Labs, he served as a research assistant at the Institute of Computer Science (ICS) of the Foundation for Research and Technology Hellas (FO.R.T.H.), and as a research engineer with the Hipercom Team at the National Institute for Research in Computer Science and Automatic Control (I.N.R.I.A.), followed by a compulsory military service in Telecommunications Division.
Bibliographic Information
Book Title: Topic Detection and Classification in Social Networks
Book Subtitle: The Twitter Case
Authors: Dimitrios Milioris
DOI: https://doi.org/10.1007/978-3-319-66414-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-66413-2Published: 13 October 2017
Softcover ISBN: 978-3-319-88238-3Published: 15 August 2018
eBook ISBN: 978-3-319-66414-9Published: 05 October 2017
Edition Number: 1
Number of Pages: XVI, 105
Number of Illustrations: 13 b/w illustrations, 25 illustrations in colour
Topics: Communications Engineering, Networks, Computational Intelligence, Computer Imaging, Vision, Pattern Recognition and Graphics, Computer Systems Organization and Communication Networks, Natural Language Processing (NLP)