Title: Measuring LDA Topic Stability from Clusters of Replicated Runs,
Abstract: Background: Unstructured and textual data is increasing rapidly and Latent
Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods
for it. Past work suggests that instability of LDA topics may lead to
systematic errors. Aim: We propose a method that relies on replicated LDA runs,
clustering, and providing a stability metric for the topics. Method: We
generate k LDA topics and replicate this process n times resulting in n*k
topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k
clusters now represent the original LDA topics and we present them like normal
LDA topics showing the ten most probable words. For the clusters, we try
multiple stability metrics, out of which we recommend Rank-Biased Overlap,
showing the stability of the topics inside the clusters. Results: We provide an
initial validation where our method is used for 270,000 Mozilla Firefox commit
messages with k=20 and n=20. We show how our topic stability metrics are
related to the contents of the topics. Conclusions: Advances in text mining
enable us to analyze large masses of text in software engineering but
non-deterministic algorithms, such as LDA, may lead to unreplicable
conclusions. Our approach makes LDA stability transparent and is also
complementary rather than alternative to many prior works that focus on LDA
parameter tuning.