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.