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

Among the many applications in social science for the entry and management of data, there are just a few software packages which apply natural language processing to identify semantic concepts such as issue categories or political statements by actors. Although these procedures usually allow an efficient data collection, most have difficulties to achieve 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, which implements an innovative coding method (Core Sentence Analysis) by computational linguistic techniques (mainly named entity recognition, concept identification, and dependency parsing). Although such computational linguistic tools are readily available since quite a long time, social scientists have made astonishingly little use of them. The principal aim of this paper 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 2003 and 2007. In the second stage, we discuss possibilities to extend our approach, for example by enriching these relations by directional measures indicating their polarity.