Home > Archive > 2021 > Volume 11 Number 2 (Mar. 2021) >
IJMLC 2021 Vol.11(2): 103-109 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.2.1021

Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews

Pumrapee Poomka, Nittaya Kerdprasop, and Kittisak Kerdprasop

Abstract—At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, na├»ve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.

Index Terms—Sentiment analysis, text classification, machine learning, deep learning.

Pumrapee Poomka, Nittaya Kerdprasop, and Kittisak Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand (e-mail: pumrapee.p@outlook.com, nittaya@sut.ac.th, kerdpras@sut.ac.th).


Cite: Pumrapee Poomka, Nittaya Kerdprasop, and Kittisak Kerdprasop, "Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 103-109, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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