Development of deep learning techniques to classify user concern for food delivery application

Abstract

With the rapid growth of the food delivery industry, there is an urgent need to manage software effectively for sharing economy applications. One way to evaluate the effectiveness of these applications is by examining user concerns and feedback. User reviews from Google Play and App Store were collected and manually labelled into four categories: bug report, human, market, and feature request. We propose to use a Bi-LSTM-CNN model in a pipeline for automatic classification of the user concerns. The performances of other machine learning and deep learning models were studied and compared. The results showed that the proposed Bi-LSTM-CNN model achieved the highest accuracy score of 84.6%, outperforming the single deep learning models and the traditional machine learning models. Moreover, due to the imbalance nature of the collected data, the impact of data oversampling technique for data imbalance problem was also evaluated. Interestingly, the interplays between the complex representation induced by the proposed Bi-LSTM-CNN model render the selected oversampling scheme e.g., SMOTE, unnecessary for our setting.