Automatic Codebook Acquisition

concepts include very useful indicators. For example: ’Judicial Power’ yields trial, court, trials, courts, jurisdiction, proceedings, evidence, law, inquiry, adjudication: all in all 10 direct synonyms and 12 indicators in the first 25 words (this is the lowest important nonpolitical actor in figure 2). 6 Summary and Discussion This paper proposes a method to aid the researcher in two steps that are performed in most content analyses: the creation of a the list of categories or entities that the researcher wants to count, and the definition of these terms in the codebook or synonym list. For the first step, our method can suggest terms with a recall of 80%-90%, with the higher figure especially for concrete political actors. Precision of the method is lower with an upper bound of around 50%. This means that the method is not suited as an automatic method to generate entities since too much noise would be contained in the result. On the other hand, the high recall means that the method can be very useful to help the researcher prevent errors of ommission. For the second step, our method can suggest lists of candidate-synonyms. In the first 25 candidates, there are on average 1.5 direct synonyms and 2.5 words that indicate the presence of the entity. In contrast to the first step, performance is best on general or abstract terms, although this might be because there simply are not many synonyms for a person.