Harmelen, F.A.H. [Promotor]van Kleinnijenhuis, J. [Promotor]Schlobach, K.S. [Copromotor

Atteveldt, W.H. van

van Atteveldt, W.H.

NARCIS 

Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content

DSpace at VU

Semantic Network Analysis
Techniques for Extracting, Representing
and Querying Media Content
Sem
anti
c N
etw
ork A
nalysis
W
outer van A
tt
eveldt Wouter van Atteveldthttp://vanatteveldt.com
http://content-analysis.org
Semantic Network Analysis is a technique in which the content of a message 
(such as a newspaper article) is extracted from text and represented as a 
network of semantic relations between actors and issues, which can be queried 
to look for speciﬁc patterns and answer various research questions. This tech-
nique yields data that are easier to compare, combine, and share than traditional 
content analysis techniques.
This books describes a number of techniques that have been developed to facili-
tate Semantic Network Analysis. It describes techniques to automatically extract 
networks using co-occurrence, grammatical analysis, and sentiment analysis . 
Additionally, it describes techniques to represent the extracted semantic 
networks and background knowledge about the actors and issues in the 
network, using Semantic Web techniques to deal with multiple issue categorisa-
tions and political roles and functions that shift over time. It shows how this com-
bined network of message content and background knowledge can be queried 
and visualized to make it easy to answer a variety of research questions. Finally, 
this book describes the AmCAT infrastructure and iNet coding program for that 
have been developed to facilitate managing large automatic and manual content 
analysis projects. 
These techniques make it easier to conduct large scale Semantic Network Analy-
sis, and to combine and share the data and results of these analyses between 
researchers. This makes it possible to create large data sets on which new or 
competing theories can be tested more easily.  
Wouter van Atteveldt wrote this thesis in an interdisciplin-
ary Ph.D. program at the depts. of Artiﬁcial Intelligence and 
Communication Science of the VU University Amsterdam 
(the Netherlands). Currently, he is employed as Post-Doc 
researcher at the dept. of Communication Science of the VU 
University, where he studies the relation between media 
content and democratic viability. He is the co-founder of LJS 
Media Research, a company specialized in media research 
using advanced content analysis techniques.


VU Research Portal

VU Research PortalSemantic Network Analysisvan Atteveldt, W.H.2008document versionPublisher's PDF, also known as Version of recordLink to publication in VU Research Portalcitation for published version (APA)van Atteveldt, W. H. (2008). Semantic Network Analysis: Techniques for Extracting, Representing, and QueryingMedia Content. BookSurge.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 22. May. 2021Semantic Network AnalysisTechniques for Extracting, Representingand Querying Media ContentSemantic Network AnalysisWouter van Atteveldt Wouter van Atteveldthttp://vanatteveldt.comhttp://content-analysis.orgSemantic Network Analysis is a technique in which the content of a message (such as a newspaper article) is extracted from text and represented as a network of semantic relations between actors and issues, which can be queried to look for specific patterns and answer various research questions. This tech-nique yields data that are easier to compare, combine, and share than traditional content analysis techniques.This books describes a number of techniques that have been developed to facili-tate Semantic Network Analysis. It describes techniques to automatically extract networks using co-occurrence, grammatical analysis, and sentiment analysis . Additionally, it describes techniques to represent the extracted semantic networks and background knowledge about the actors and issues in the network, using Semantic Web techniques to deal with multiple issue categorisa-tions and political roles and functions that shift over time. It shows how this com-bined network of message content and background knowledge can be queried and visualized to make it easy to answer a variety of research questions. Finally, this book describes the AmCAT infrastructure and iNet coding program for that have been developed to facilitate managing large automatic and manual content analysis projects. These techniques make it easier to conduct large scale Semantic Network Analy-sis, and to combine and share the data and results of these analyses between researchers. This makes it possible to create large data sets on which new or competing theories can be tested more easily.  Wouter van Atteveldt wrote this thesis in an interdisciplin-ary Ph.D. program at the depts. of Artificial Intelligence and Communication Science of the VU University Amsterdam (the Netherlands). Currently, he is employed as Post-Doc researcher at the dept. of Communication Science of the VU University, where he studies the relation between media content and democratic viability. He is the co-founder of LJS Media Research, a company specialized in media research using advanced content analysis techniques.

English