Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing
DOI:
https://doi.org/10.26905/jtmi.v7i2.6506Keywords:
Model Comparison, Deep Learning, Sentiment Analysis, Natural Language Processing.Abstract
Everyone has an opinion or opinion on a product, public figure, or government policy that is spread on social media. Opinion data processing is called sentiment analysis. In processing large opinion data, it is not enough to only use machine learning, but you can also use deep learning combined with NLP (Natural Language Processing) techniques. This study compares several deep learning models such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), and several variants to process sentiment analysis data from Amazon and Yelp product reviews.References
J. Hurwitz and D. Kirsch, Machine Learning For Dummies IBM Limited Edition, 2018th ed. New Jersey: John Wiley & Sons, Inc, 2018.
Suyanto, K. Ramadhani Nur, and S. Mandala, Deep Learning Modernisasi Machine Learning untuk Big Data. Bandung: INFORMATIKA, 2019.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge: The MIT Press, 2016.
M. Z. Alom et al., “The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches,†2018, [Online]. Available: http://arxiv.org/abs/1803.01164.
A. Jacovi, O. Sar Shalom, and Y. Goldberg, “Understanding Convolutional Neural Networks for Text Classification,†no. January, pp. 56–65, 2019, doi: 10.18653/v1/w18-5408.
F. CHOLLET, Deep Learning with Phyton. New York: Manning Publications Co., 2018.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,†Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,†IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673–2681, 1997, doi: 10.1109/78.650093.
M. Basaldella, E. Antolli, G. Serra, and C. Tasso, “Bidirectional LSTM recurrent neural network for keyphrase extraction,†Commun. Comput. Inf. Sci., vol. 806, no. January, pp. 180–187, 2018, doi: 10.1007/978-3-319-73165-0_18.
J. Pustejovsky and A. Stubbs, Natural Language Annotation for Machine Learning -- A guide to Corpus-building for applications. 2013.
S. Suryono, E. Utami, and E. T. Luthfi, “Klasifikasi Sentimen Pada Twitter Dengan Naive Bayes Classifier,†Angkasa J. Ilm. Bid. Teknol., vol. 10, no. 1, p. 89, 2018, doi: 10.28989/angkasa.v10i1.218.
Y. D. Prabowo, H. L. H. S. Warnars, W. Budiharto, A. I. Kistijantoro, Y. Heryadi, and Lukas, “Lstm and Simple Rnn Comparison in the Problem of Sequence to Sequence on Conversation Data Using Bahasa Indonesia,†1st 2018 Indones. Assoc. Pattern Recognit. Int. Conf. Ina. 2018 - Proc., no. November 2020, pp. 51–56, 2019, doi: 10.1109/INAPR.2018.8627029.
M. J. Budhwar, “Sentiment Analysis based Method for Amazon Product Reviews,†pp. 54–57, 2021.
A. Nurdin, B. Anggo Seno Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan Fasttext Pada Klasifikasi Teks,†J. Tekno Kompak, vol. 14, no. 2, p. 74, 2020, doi: 10.33365/jtk.v14i2.732.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,†Adv. Neural Inf. Process. Syst., vol. 4, no. January, pp. 3104–3112, 2014.
Downloads
Additional Files
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
(1)Â Copyright of the published articles will be transferred to the journal as the publisher of the manuscripts. Therefore, the author confirms that the copyright has been managed by the journal.
(2) Publisher of JTMI: Jurnal Teknologi dan Manajemen Informatika is University of Merdeka Malang.
(3) The copyright follows Creative Commons Attribution–ShareAlike License (CC BY SA): This license allows to Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material, for any purpose, even commercially.
