  The energy inefficiency of the apps can be a major issue for the app users
which is discussed on App Stores extensively. Previous research has shown the
importance of investigating the energy related app reviews to identify the
major causes or categories of energy related user feedback. However, there is
no study that efficiently extracts the energy related app reviews
automatically. In this paper, we empirically study different techniques for
automatic extraction of the energy related user feedback. We compare the
accuracy, F1-score and run time of numerous machine-learning models with
relevant feature combinations and relatively modern Neural Network-based
models. In total, 60 machine learning models are compared to 30 models that we
build using six neural network architectures and three word embedding models.
We develop a visualization tool for this study through which a developer can
traverse through this large-scale result set. The results show that neural
networks outperform the other machine learning techniques and can achieve the
highest F1-score of 0.935. To replicate the research results, we have open
sourced the interactive visualization tool. After identifying the best results
and extracting the energy related reviews, we further compare various
techniques to help the developers automatically investigate the emerging issues
that might be responsible for energy inefficiency of the apps. We experiment
the previously used string matching with results obtained from applying two of
the state-of-the-art topic modeling algorithms, OBTM and AOLDA. Finally, we run
a qualitative study performed in collaboration with developers and students
from different institutions to determine their preferences for identifying
necessary topics from previously categorized reviews, which shows OBTM produces
the most helpful results.
