Characterizing User Decision based on Argumentative Reviews

説明

Opinion mining from mobile app reviews has grown exponentially during the last decade. Most studies in this area, however, have focused on a sentiment analysis. In this study, we consider review mining from another perspective, that is, capturing user justifications behind whatever actions are explicitly stated in their app reviews, e.g., the reason behind user purchases. This study highlights how different app features can promote different user decisions, which in turn can be beneficial for software developers to gain valuable data-driven requirements for the planning and development of application updates. We collected, used, and shared our manually annotated 46k mobile app reviews from 12 different app categories in the Google Play Store. We designed three classification problems to filter reviews containing both arguments and decisions from non-argumentative reviews. We extracted three features, namely, structural, lexical, and contextual, from the body of review sentences. Four classifiers (naive Bayes, logistic regression, support vector machine, and random forest) and different feature combinations were trained on the dataset and evaluated to examine if such features can allow us to classify user arguments and decisions. The results show an improved performance over previous studies and show the efficacy of the proposed approach compared to human-assessments.

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