Header

UZH-Logo

Maintenance Infos

Semi-automatic core sentence analysis: improving content analysis for electoral campaign research


Wüest, Bruno; Clematide, Simon; Bünzli, Alexandra; Laupper, Daniel (2010). Semi-automatic core sentence analysis: improving content analysis for electoral campaign research. In: JMCE/RECON Workshop ‘Computer-aided methods of textual analysis', Berlin, 27 May 2010 - 28 May 2010.

Abstract

Most automated procedures used for the analysis of textual data do not apply natural language processing techniques. While these applications usually allow for an efficient data collection, most have difficulties to achieve sufficient accuracy because of the high complexity and interdependence of semantic concepts used in the social sciences. Manual content analysis approaches sometimes lack accuracy too, but, more virulently, human coding entails a heavy workload for the researcher. To address this high cost problem without running into the risk of oversimplification, we suggest a semi-automatic approach. Our application implements an innovative coding method based on computational linguistic techniques, i.e. mainly named entity recognition and concept identification. In order to show the potential of this new method, we apply it to an analysis of electoral campaigns. In the first stage of this contribution, we describe how relations between political parties and issues can be recognized by an automated system. In the second stage, we discuss facilities to manually attribute a positive or negative direction to these relations.

Abstract

Most automated procedures used for the analysis of textual data do not apply natural language processing techniques. While these applications usually allow for an efficient data collection, most have difficulties to achieve sufficient accuracy because of the high complexity and interdependence of semantic concepts used in the social sciences. Manual content analysis approaches sometimes lack accuracy too, but, more virulently, human coding entails a heavy workload for the researcher. To address this high cost problem without running into the risk of oversimplification, we suggest a semi-automatic approach. Our application implements an innovative coding method based on computational linguistic techniques, i.e. mainly named entity recognition and concept identification. In order to show the potential of this new method, we apply it to an analysis of electoral campaigns. In the first stage of this contribution, we describe how relations between political parties and issues can be recognized by an automated system. In the second stage, we discuss facilities to manually attribute a positive or negative direction to these relations.

Statistics

Downloads

1 download since deposited on 20 Mar 2018
1 download since 12 months
Detailed statistics

Additional indexing

Other titles:Electoral campaign and relation mining: extracting semantic network data from swiss newspaper articles
Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
Dewey Decimal Classification:320 Political science
Language:English
Event End Date:28 May 2010
Deposited On:20 Mar 2018 13:18
Last Modified:13 Apr 2018 11:43
OA Status:Green
Related URLs:http://www.polsoz.fu-berlin.de/en/polwiss/forschung/international/jmce/events/Workshop_May_2010.html (Organisation)

Download

Download PDF  'Semi-automatic core sentence analysis: improving content analysis for electoral campaign research'.
Preview
Content: Submitted Version
Language: English
Filetype: PDF
Size: 1MB