  Requirements elicitation has recently been complemented with crowd-based
techniques, which continuously involve large, heterogeneous groups of users who
express their feedback through a variety of media. Crowd-based elicitation has
great potential for engaging with (potential) users early on but also results
in large sets of raw and unstructured feedback. Consolidating and analyzing
this feedback is a key challenge for turning it into sensible user
requirements. In this paper, we focus on topic modeling as a means to identify
topics within a large set of crowd-generated user stories and compare three
approaches: (1) a traditional approach based on Latent Dirichlet Allocation,
(2) a combination of word embeddings and principal component analysis, and (3)
a combination of word embeddings and Word Mover's Distance. We evaluate the
approaches on a publicly available set of 2,966 user stories written and
categorized by crowd workers. We found that a combination of word embeddings
and Word Mover's Distance is most promising. Depending on the word embeddings
we use in our approaches, we manage to cluster the user stories in two ways:
one that is closer to the original categorization and another that allows new
insights into the dataset, e.g. to find potentially new categories.
Unfortunately, no measure exists to rate the quality of our results
objectively. Still, our findings provide a basis for future work towards
analyzing crowd-sourced user stories.

Computation and Language;Information Retrieval