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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(5): 437-441 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.5.725

Opinion Mining on Book Review Using Convolutional Neural Network Algorithm — Long Short Term Memory

Imam Mukhlash, Anshar Zamrudillah Arham, Fakhrur Rozi, Masaomi Kimura, and Dieky Adzkiya
Abstract—The quality of a product can be determined by consumer reviews of the product because consumer opinion is in general honest and sentimental to the product. The process of opinion extraction is called opinion mining. This process is done to determine the tendency of the reviewer to the reviewed object. Deep Learning is a recently developed opinion extraction model. This model is widely used for achieving performance in Natural Language Processing. This work classifies the book review on Amazon.com into positive reviews or negative reviews using a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) algorithms. Simulation results show that the performance of this algorithm is approximately 65.03% for testing data and 99.55% for training data.

Index Terms—Book review, deep learning, long short term memory, opinion mining.

I. Mukhlash, A. Z. Arham, F. Rozi and D. Adzkiya are with the Department of Mathematics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo-Surabaya 60111, Surabaya, Indonesia (e-mail: imamm@matematika.its.ac.id).
M. Kimura is with Department of Information Science and Engineering, School of Engineering, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto City, Tokyo 135-8548, Japan (e-mail: masaomi@shibaura-it.ac.jp).

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Cite: Imam Mukhlash, Anshar Zamrudillah Arham, Fakhrur Rozi, Masaomi Kimura, and Dieky Adzkiya, "Opinion Mining on Book Review Using Convolutional Neural Network Algorithm — Long Short Term Memory," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 437-441, 2018.

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