ANALYSING JOURNALISTIC DISCOURSE AND FINDING OPINIONS SEMI-AUTOMATICALLY?

Fabienne H. Baider (1)

1 University of Cyprus = Université de Chypre

In this research we suggest that working on a journalistic corpus with specific softwares can help studying linguistic patterns and choices which are made on the basis of political affiliation or gender stereotypes. The software SEMY for instance gives semantic profiles semi-automatically, ANTCONC gives useful KWIC abstracts and TERMOSTAT works on discourse specificities. Using these tools we found convergent asymmetries between female and male candidates in journalistic discourse (however conditionally) as far as our corpus dedicated to the 2007 and the 2012 presidential campaigns are concerned. Social gender' (i.e. stereotypical expectations about who will be a typical member of a given category) and / or political favoritism may then affect the representation of leadership in discourse and may affect in turn the readership, hence the electorate.

Mots clés: semi-automatic reading; gender stereotypes; political favoritism; journalistic discourse; French presidential campaign

Mots clés: lecture automatique; stéréotypes de genre; biais politique; discours journalistique; campagne présidentielle française

Domaines: Linguistique

Fabienne H. Baider. ANALYSING JOURNALISTIC DISCOURSE AND FINDING OPINIONS SEMI-AUTOMATICALLY?: A CASE STUDY OF THE 2007 AND 2012 PRESIDENTIAL FRENCH CAMPAIGNS. 2014. ⟨hal-00919370v2⟩