Identifying salient political categories in a legislative debate: A text-mining approach to discourse analysis

2013 ◽  
Author(s):  
Cristina J. Montiel ◽  
Ma. Regina E. Estuar ◽  
Audris P. Umel
Pragmatics ◽  
2011 ◽  
Vol 21 (4) ◽  
pp. 647-683 ◽  
Author(s):  
Senja Pollak ◽  
Roel Coesemans ◽  
Walter Daelemans ◽  
Nada Lavrač

Text mining aims at constructing classification models and finding interesting patterns in large text collections. This paper investigates the utility of applying these techniques to media analysis, more specifically to support discourse analysis of news reports about the 2007 Kenyan elections and post-election crisis in local (Kenyan) and Western (British and US) newspapers. It illustrates how text mining methods can assist discourse analysis by finding contrast patterns which provide evidence for ideological differences between local and international press coverage. Our experiments indicate that most significant differences pertain to the interpretive frame of the news events: whereas the newspapers from the UK and the US focus on ethnicity in their coverage, the Kenyan press concentrates on sociopolitical aspects.


2020 ◽  
Vol 107 (2) ◽  
pp. 141-157
Author(s):  
Emma Elisabeth Kiis

AbstractThis article uses messages communicated through the Islamic State’s propaganda magazine, Rumiyah, to explore the applicability of text mining methods in discourse analysis. The repertoire of narratives used in Rumiyah is examined through the theoretical framework of Narrative Criminology in combination with Discourse Theory, as presented by Ernesto Laclau and Chantal Mouffe. Techniques and methods from the field of digital text mining are also applied. The current article therefore has two sections: a quantitatively-deduced discourse analysis and a qualitatively-deduced discourse analysis.


Author(s):  
Stanley Loh ◽  
Leandro Krug Wives ◽  
Daniel Lichtnow ◽  
José Palazzo M. de Oliveira

The goal of this chapter is to present an approach to mine texts through the analysis of higher level characteristics (called “concepts’), minimizing the vocabulary problem and the effort necessary to extract useful information. Instead of applying text mining techniques on terms or keywords labeling or extracted from texts, the discovery process works over concepts extracted from texts. Concepts represent real world attributes (events, objects, feelings, actions, etc.) and, as seen in discourse analysis, they help to understand ideas and ideologies present in texts. A previous classification task is necessary to identify concepts inside the texts. After that, mining techniques are applied over the concepts discovered. The chapter will discuss different concept-based text mining techniques and present results from different applications.


Author(s):  
Zhifeng Wang ◽  
◽  
Rong Zhao ◽  
Yanli Xu ◽  
Xiangyong Li ◽  
...  

2019 ◽  
Vol 42 ◽  
Author(s):  
Giulia Frezza ◽  
Pierluigi Zoccolotti

Abstract The convincing argument that Brette makes for the neural coding metaphor as imposing one view of brain behavior can be further explained through discourse analysis. Instead of a unified view, we argue, the coding metaphor's plasticity, versatility, and robustness throughout time explain its success and conventionalization to the point that its rhetoric became overlooked.


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