Abstract
This study is related to assist for finding the mental health Apps on Google Play Store using text reviews and rating. The apps are related to Anxiety, Bipolar disorder, Epilepsy, Migraine and Mental retarded. A prediction system has been proposed using Tri-Gram features and Logistic Regression. The system is trained using reviews as features and rating as labels/classes. The model has been evaluated using accuracy, precision, recall and f1-score. It is also compared with other state-of-the-art classifiers, but the proposed approach provides the good accuracy. The accuracy, precision, recall and f1-score of the proposed model respectively are 0.69, 0.67, 0.69 and 0.66. The results exhibited that the Logistic Regression out-performs as compare to other state-of-the-art algorithms.
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Acknowledgments
This study has been supported in part of the Fundamental Research Grant Scheme (FRGS) Vot K006 under the Malaysia Ministry of Higher Education (MOHE) and Universiti Tun Hussein Onn Malaysia (UTHM).
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Ahmad, M. et al. (2020). Mental Health App Reviews Analyzer (MHARA) Using Logistic Regression and Tri-Gram. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_27
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DOI: https://doi.org/10.1007/978-3-030-36056-6_27
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