Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning

Authors

  • Ahmad Rais Dwijaya Universitas Amikom Yogyakarta
  • Arif Dwi Laksito Universitas Amikom Yogyakarta
(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v5i2.207

Keywords:

Deep Learning, LSTM, Machine Learning, Sentiment Analysis

Abstract

The COVID-19 pandemic that hit the world at the end of early 2020 caused many losses. The Indonesian government has established various ways to reduce the path of the COVID-19 pandemic by launching the PeduliLindungi application to reduce the spread of COVID-19. Various layers of society responded to the launch of the application with various opinions. This research mainly analyzes public opinion sentiment toward the PeduliLindungi application, as determined by 10,000 reviews on the Google Play Store. This study aims to compare the performance of deep learning and machine learning models in sentiment analysis. The stages of the research method begin with data collection methods, data pre-processing, and sentiment analysis using a machine learning model with the embedding of the word TF-IDF, which includes the Nave Bayes algorithm, Decision Tree, Random Forest, K-Nearest Neighbour, and SVM. As for the deep learning model with the fastText word embedding word representation technique using the LSTM algorithm, an evaluation is carried out using the confusion matrix. The results of this study state that deep learning models perform better than machine learning models.

Downloads

Download data is not yet available.

References

AlSurayyi, W. I., Alghamdi, N. S., & Abraham, A. (2019). Deep learning with word embedding modeling for a sentiment analysis of online reviews. International Journal of Computer Information Systems and Industrial Management Applications, 11, 227–241. Retrieved from http://www.mirlabs.org/ijcisim/regular_papers_2019/IJCISIM_22.pdf

Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Transactions of the Association for Computational Linguistics. Transactions of the Association for Computational Linguistics, 5, 135–146. Retrieved from https://transacl.org/ojs/index.php/tacl/article/view/999

Botrè, C., Lucarini, C., Memoli, A., & D’Ascenzo, E. (1981). 397 - On the entropy production in oscillating chemical systems. Bioelectrochemistry and Bioenergetics, 8(2), 201–212. https://doi.org/10.1016/0302-4598(81)80041-4

Brennan, P. M., Loan, J. J. M., Watson, N., Bhatt, P. M., & Bodkin, P. A. (2017). Pre-operative obesity does not predict poorer symptom control and quality of life after lumbar disc surgery. British Journal of Neurosurgery, 31(6), 682–687. https://doi.org/10.1080/02688697.2017.1354122

Deho, O. B., Agangiba, W. A., Aryeh, F. L., & Ansah, J. A. (2018). Sentiment analysis with word embedding. 2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST), 1–4. https://doi.org/10.1109/ICASTECH.2018.8506717

Imaduddin, H., Widyawan, & Fauziati, S. (2019). Word embedding comparison for Indonesian language sentiment analysis. 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 426–430. https://doi.org/10.1109/ICAIIT.2019.8834536

Jones, K. S. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21. https://doi.org/10.1108/eb026526

Kamiş, S., & Goularas, D. (2019). Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), 12–17. https://doi.org/10.1109/Deep-ML.2019.00011

Kilimci, Z. H., & Akyokus, S. (2019). The Evaluation of Word Embedding Models and Deep Learning Algorithms for Turkish Text Classification. 2019 4th International Conference on Computer Science and Engineering (UBMK), 548–553. IEEE. https://doi.org/10.1109/UBMK.2019.8907027

Marukatat, R. (2020). A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classification of English and Thai Text. In Studies in Computational Intelligence (Vol. 847). Springer International Publishing. https://doi.org/10.1007/978-3-030-25217-5_7

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, 1–12. Retrieved from https://arxiv.org/abs/1711.08609

Rahman, M. Z., Sari, Y. A., & Yudistira, N. (2021). Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(11), 5120–5127. Retrieved from http://j-ptiik.ub.ac.id

Rezaeinia, S. M., Ghodsi, A., & Rahmani, R. (2017). Improving the accuracy of pre-trained word embeddings for sentiment analysis. ArXiv, 1–15. Retrieved from https://arxiv.org/abs/1711.08609

Wang, C., Nulty, P., & Lillis, D. (2020). A Comparative Study on Word Embeddings in Deep Learning for Text Classification. Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval, 37–46. https://doi.org/10.1145/3443279.3443304

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing [Review Article]. IEEE Computational Intelligence Magazine, 13(3), 55–75. https://doi.org/10.1109/MCI.2018.2840738

Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent Neural Network Regularization. ArXiv, (2013), 1–8. Retrieved from http://arxiv.org/abs/1409.2329

Downloads

Published

2023-03-25

How to Cite

Dwijaya, A. R., & Laksito, A. D. (2023). Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning. Jurnal Riset Informatika, 5(2), 187–194. https://doi.org/10.34288/jri.v5i2.207

Issue

Section

Articles