Main Article Content
The app reviews are useful for app developers because they contain valuable information, e.g. bug, feature request, user experience, and rating. This information can be used to better understand user needs and application defects during software maintenance and evolution phase. The increasing number of reviews causes problems in the analysis process for developers. Reviews in textual form are difficult to understand, this is due to the difficulty of considering semantic between sentences. Moreover, manual checking is time-consuming, requires a lot of effort, and costly for manual analysis. Previous research shows that the collection of the review contains non-informative reviews because they do not have valuable information. Non-informative reviews considered as noise and should be eliminated especially for classification process. Moreover, semantic problems between sentences are not considered for the reviews classification. The purpose of this research is to classify user reviews into three classes, i.e. bug, feature request, and non-informative reviews automatically. User reviews are converted into vectors using word embedding to handle the semantic problem. The vectors are used as input into the first classifier that classifies informative and non-informative reviews. The results from the first classifier, that is informative reviews, then reclassified using the second classifier to determine its category, e.g. bug report or feature request. The experiment using 306,849 sentences of reviews crawled from Google Play and F-Droid. The experiment result shows that the proposed model is able to classify mobile application review by produces best accuracy of 0.79, precision of 0.77, recall of 0.87, and F-Measure of 0.81.
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