Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230765
Authors: 
Pietsch, Andra-Selina
Lessmann, Stefan
Year of Publication: 
2018
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2018-054
Abstract: 
Open-ended responses are widely used in market research studies. Processing of such responses requires labor-intensive human coding. This paper focuses on unsupervised topic models and tests their ability to automate the analysis of open-ended responses. Since state-of-the-art topic models struggle with the shortness of open-ended responses, the paper considers three novel short text topic models: Latent Feature Latent Dirichlet Allocation, Biterm Topic Model and Word Network Topic Model. The models are fitted and evaluated on a set of realworld open-ended responses provided by a market research company. Multiple components such as topic coherence and document classification are quantitatively and qualitatively evaluated to appraise whether topic models can replace human coding. The results suggest that topic models are a viable alternative for open-ended response coding. However, their usefulness is limited when a correct one-to-one mapping of responses and topics or the exact topic distribution is needed.
Subjects: 
Market research
open-ended responses
text analytics
short text topic models
JEL: 
C00
Document Type: 
Working Paper

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