Abstract
An app store (i.e., Google Play ) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user’s experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and non-textual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD , and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.
Highlights
In this digital world, softwares are moved from computers to mobile phones
The approach works as follows: 1) it extracts textual and non-textual features (i.e., the statistics of the reviewer, and the statistics of each app review; 2), it preprocesses the textual information and transforms it into a digital vector; and 3) it trains a Convolutional Neural Network (CNN) classifier to classify multi-class reviews
EVALUATION the proposed deep learning-based classification approach (DCAR) for reviews is evaluated with the real-world reviews from Google Play and Apple app stores
Summary
Softwares are moved from computers to mobile phones. App stores (i.e., Google Play Store and Apple AppStore) provide mobile apps (noted as apps for short in the rest of this paper) almost for every field of life. Such approaches consider app reviews as plain texts, their metadata (e.g., length of text) and sentiment, and employ traditional machine learning techniques to make the prediction To further improve their performance, in this paper, we propose a Convolutional Neural Network (CNN) based approach for the classification of app reviews. The approach works as follows: 1) it extracts textual (i.e., the textual information, and the sentiment of the textual information computed by Senti4SD) and non-textual features (i.e., the statistics of the reviewer (the total number of reviews posted by the reviewer, and his submission rate, i.e., what percentages of his review have been submitted for the associated app), and the statistics (metadata) of each app review; 2), it preprocesses the textual information and transforms it into a digital vector; and 3) it trains a CNN classifier to classify multi-class reviews. Details of the proposed approach are presented
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