  Improving the energy efficiency of mobile applications is a topic that has
gained a lot of attention recently. It has been addressed in a number of ways
such as identifying energy bugs and developing a catalog of energy patterns.
Previous work shows that users discuss the battery-related issues (energy
inefficiency or energy consumption) of the apps in their reviews. However,
there is no work that addresses the automatic extraction of battery-related
issues from users' feedback. In this paper, we report on a visualization tool
that is developed to empirically study machine learning algorithms and text
features to automatically identify the energy consumption specific reviews with
the highest accuracy. Other than the common machine learning algorithms, we
utilize deep learning models with different word embeddings to compare the
results. Furthermore, to help the developers extract the main topics that are
discussed in the reviews, two states of the art topic modeling algorithms are
applied. The visualizations of the topics represent the keywords that are
extracted for each topic along with a comparison with the results of string
matching. The developed web-browser based interactive visualization tool is a
novel framework developed with the intention of giving the app developers
insights about running time and accuracy of machine learning and deep learning
models as well as extracted topics. The tool makes it easier for the developers
to traverse through the extensive result set generated by the text
classification and topic modeling algorithms. The dynamic-data structure used
for the tool stores the baseline-results of the discussed approaches and is
updated when applied on new datasets. The tool is open-sourced to replicate the
research results.
