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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Abstract

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Change history

  • 15 February 2023

    Correct Email address of author Kewen Xia has been updated in original version

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. U1813222, No. 42075129), Hebei Province Natural Science Foundation (No. E2021202179), Key Research and Development Project from Hebei Province (No. 19210404D, No. 20351802D, No.21351803D).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China

    Naila Aslam & Kewen Xia

  2. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Ohoud Alzamzami

  3. Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

    Saima Sadiq

  4. Department of Computer Science Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Muhammad Umer

  5. Department of Computer Science, University of Salerno, Fisciano, Italy

    Carmen Bisogni

  6. Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea

    Imran Ashraf

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  1. Naila Aslam
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Correspondence to Kewen Xia or Carmen Bisogni.

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Aslam, N., Alzamzami, O., Xia, K. et al. Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model. J Ambient Intell Human Comput 14, 4257–4272 (2023). https://doi.org/10.1007/s12652-023-04529-5

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