skip to content
Electoral Campaigns and Relation Mining: Extracting Semantic Network Data from Newspaper Articles
ClosePreview this item
Checking...

Electoral Campaigns and Relation Mining: Extracting Semantic Network Data from Newspaper Articles

Author: Bruno Wueest; Simon Clematide; Alexandra Bunzli; Daniel Laupper; Timotheos Frey
Publisher: Taylor & Francis
Edition/Format: Article Article : EN
Publication:Journal of Information Technology & Politics, 8, no. 4 (2011): 444-463
Database:ArticleFirst
Other Databases: ElsevierBritish Library Serials
Rating:

(not yet rated) 0 with reviews - Be the first.

More like this

 

&AllPage.SpinnerRetrieving;

Find a copy online

Links to this journal/publication

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Document Type: Article
All Authors / Contributors: Bruno Wueest; Simon Clematide; Alexandra Bunzli; Daniel Laupper; Timotheos Frey
ISSN:1933-1681
Language Note: EN
Unique Identifier: 750098677
Awards:

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

All user tags (1)

View most popular tags as: tag list | tag cloud

  • 2011  (by 1 person)

Similar Items

User lists with this item (2)

Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


<http://www.worldcat.org/oclc/750098677>
library:oclcnum"750098677"
owl:sameAs<info:oclcnum/750098677>
rdf:typeschema:Article
schema:contributor
schema:contributor
schema:contributor
schema:contributor
schema:creator
schema:datePublished"2011-10-01"
schema:description"Among the many applications in social science for the entry and management of data, there are only a few software packages that apply natural language processing to identify semantic concepts such as issue categories or political statements by actors. Although these procedures usually allow efficient data collection, most have difficulty in achieving sufficient accuracy because of the high complexity and mutual relationships of the variables used in the social sciences. To address these flaws, we suggest a (semi-) automatic annotation approach that implements an innovative coding method (Core Sentence Analysis) by computational linguistic techniques (mainly entity recognition, concept identification, and dependency parsing). Although such computational linguistic tools have been readily available for quite a long time, social scientists have made astonishingly little use of them. The principal aim of this article is to gather data on party-issue relationships from newspaper articles. In the first stage, we try to recognize relations between parties and issues with a fully automated system. This recognition is extensively tested against manually annotated data of the coverage in the boulevard newspaper Blick of the Swiss national parliamentary elections of 2003 and 2007. In the second stage, we discuss possibilities for extending our approach, such as by enriching these relations with directional measures indicating their polarity."
schema:exampleOfWork<http://worldcat.org/entity/work/id/1007605908>
schema:isPartOf
schema:isPartOf
<http://worldcat.org/issn/1933-1681>
rdf:typeschema:Periodical
schema:name"Journal of Information Technology & Politics"
schema:name"Electoral Campaigns and Relation Mining: Extracting Semantic Network Data from Newspaper Articles"
schema:pageStart"444"
schema:publisher
schema:url

Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.