  Analysis of mobile app reviews has shown its important role in requirement
engineering, software maintenance and evolution of mobile apps. Mobile app
developers check their users' reviews frequently to clarify the issues
experienced by users or capture the new issues that are introduced due to a
recent app update. App reviews have a dynamic nature and their discussed topics
change over time. The changes in the topics among collected reviews for
different versions of an app can reveal important issues about the app update.
A main technique in this analysis is using topic modeling algorithms. However,
app reviews are short texts and it is challenging to unveil their latent topics
over time. Conventional topic models suffer from the sparsity of word
co-occurrence patterns while inferring topics for short texts. Furthermore,
these algorithms cannot capture topics over numerous consecutive time-slices.
Online topic modeling algorithms speed up the inference of topic models for the
texts collected in the latest time-slice by saving a fraction of data from the
previous time-slice. But these algorithms do not analyze the statistical-data
of all the previous time-slices, which can confer contributions to the topic
distribution of the current time-slice.
  We propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in
short texts adaptively. AOBTM alleviates the sparsity problem in short-texts
and considers the statistical-data for an optimal number of previous
time-slices. We also propose parallel algorithms to automatically determine the
optimal number of topics and the best number of previous versions that should
be considered in topic inference phase. Automatic evaluation on collections of
app reviews and real-world short text datasets confirm that AOBTM can find more
coherent topics and outperforms the state-of-the-art baselines.
