  Understanding the semantic of a collection of texts is a challenging task.
Topic models are probabilistic models that aims at extracting "topics" from a
corpus of documents. This task is particularly difficult when the corpus is
composed of short texts, such as posts on social networks. Following several
previous research papers, we explore in this paper a set of collected tweets
about bitcoin. In this work, we train three topic models and evaluate their
output with several scores. We also propose a concrete application of the
extracted topics.
