  App reviews are crowdsourcing knowledge of user experience with the apps,
providing valuable information for app release planning, such as major bugs to
fix and important features to add. There exist prior explorations on app review
mining for release planning, however, most of the studies strongly rely on
pre-defined classes or manually-annotated reviews. Also, the new review
characteristic, i.e., the number of users who rated the review as helpful,
which can help capture important reviews, has not been considered previously.
  In the paper, we propose a novel framework, named SOLAR, aiming at accurately
summarizing helpful user reviews to developers. The framework mainly contains
three modules: The review helpfulness prediction module, topic-sentiment
modeling module, and multi-factor ranking module. The review helpfulness
prediction module assesses the helpfulness of reviews, i.e., whether the review
is useful for developers. The topic-sentiment modeling module groups the topics
of the helpful reviews and also predicts the associated sentiment, and the
multi-factor ranking module aims at prioritizing semantically representative
reviews for each topic as the review summary. Experiments on five popular apps
indicate that SOLAR is effective for review summarization and promising for
facilitating app release planning.
