  In this paper, we explore the application of language and speech technology
to open-ended questions in a Dutch panel survey. In an experimental wave
respondents could choose to answer open questions via speech or keyboard.
Automatic speech recognition (ASR) was used to process spoken responses. We
evaluated answers from these input modalities to investigate differences
between spoken and typed answers.We report the errors the ASR system produces
and investigate the impact of these errors on downstream analyses. Open-ended
questions give more freedom to answer for respondents, but entail a non-trivial
amount of work to analyse. We evaluated the feasibility of using
transformer-based models (e.g. BERT) to apply sentiment analysis and topic
modelling on the answers of open questions. A big advantage of
transformer-based models is that they are trained on a large amount of language
materials and do not necessarily need training on the target materials. This is
especially advantageous for survey data, which does not contain a lot of text
materials. We tested the quality of automatic sentiment analysis by comparing
automatic labeling with three human raters and tested the robustness of topic
modelling by comparing the generated models based on automatic and manually
transcribed spoken answers.
