  We use commercially available text analysis technology to process interview
text data from a computational social science study. We find that topical
clustering and terminological enrichment provide for convenient exploration and
quantification of the responses. This makes it possible to generate and test
hypotheses and to compare textual and non-textual variables, and saves analyst
effort. We encourage studies in social science to use text analysis, especially
for exploratory open-ended studies. We discuss how replicability requirements
are met by text analysis technology. We note that the most recent learning
models are not designed with transparency in mind, and that research requires a
model to be editable and its decisions to be explainable. The tools available
today, such as the one used in the present study, are not built for processing
interview texts. While many of the variables under consideration are
quantifiable using lexical statistics, we find that some interesting and
potentially valuable features are difficult or impossible to automatise
reliably at present. We note that there are some potentially interesting
applications for traditional natural language processing mechanisms such as
named entity recognition and anaphora resolution in this application area. We
conclude with a suggestion for language technologists to investigate the
challenge of processing interview data comprehensively, especially the
interplay between question and response, and we encourage social science
researchers not to hesitate to use text analysis tools, especially for the
exploratory phase of processing interview data.?
