Title: Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries,
Abstract: This paper presents a procedure to retrieve subsets of relevant documents
from large text collections for Content Analysis, e.g. in social sciences.
Document retrieval for this purpose needs to take account of the fact that
analysts often cannot describe their research objective with a small set of key
terms, especially when dealing with theoretical or rather abstract research
interests. Instead, it is much easier to define a set of paradigmatic documents
which reflect topics of interest as well as targeted manner of speech. Thus, in
contrast to classic information retrieval tasks we employ manually compiled
collections of reference documents to compose large queries of several hundred
key terms, called dictionaries. We extract dictionaries via Topic Models and
also use co-occurrence data from reference collections. Evaluations show that
the procedure improves retrieval results for this purpose compared to
alternative methods of key term extraction as well as neglecting co-occurrence
data.