Performance Improvement Using CNN for Sentiment Analysis

Moch. Ari Nasichuddin(1*), Teguh Bharata Adji(2), Widyawan Widyawan(3)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author


The approach using Deep Learning method provides great results in various field implementations, especially in the field of Sentiment Analysis. One of Deep Learning methods is CNN which has the ability to provide great accuracy in some previous research. However, there are some parts of the training process which can be improved to upgrade the accuracy level and the training time. In this paper, we try to improve the accuracy and processing time of sentiment analysis using CNN model. By tuning the filter size, frameworks, and pre-training, the results show that the use of smaller filter size and pre-training word2vec provide greater accuracy than some previous studies.


CNN, Deep Learning, Sentiment Analysis

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L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. New Jersey: Prentice-Hall Inc, 1994.

J. M. Zurada, Introduction to Artificial Neural Systems. St. Paul: West Publishing Co., 1992.

D. Puspatiningrum, Pengantar Jaringan Saraf Tiruan. Yogyakarta: Penerbit Andi, 2006.

Y. Kim, “Convolutional Neural Networks for Sentence Classification,” Proc. of the 2014 Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1746–1751.

Y. Zhang and B. Wallace, “A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification,” arXiv:1510.03820 [cs.CL], 2015.

A. Hassan and A. Mahmood, “Deep Learning Approach for Sentiment Analysis of Short Texts,” 2017 3rd Int. Conf. Control. Autom. Robot, 2017, pp. 705–710.

K. G. Pasi and S. R. Naik, “Effect of Parameter Variations on Accuracy of Convolutional Neural Network,” Int. Conf. Comput. Anal. Secur. Trends, CAST 2016, 2017, pp. 398–403.

X. Ouyang, P. Zhou, C. H. Li, and L. Liu, “Sentiment Analysis Using Convolutional Neural Network,” 2015 IEEE Int. Conf. Comput. Inf. Technol. Ubiquitous Comput. Commun. Dependable, Auton. Secur. Comput. Pervasive Intell. Comput., 2015, pp. 2359–2364.

B. Pang, (2002) “Movie Review Data,” [Online],, accessed: 20-May-2017.

R. Socher, (2013) “Stanford Sentiment Treebank,” [Online]. Available:, accessed: 20-May-2017.

(2013) “Word2vec,” [Online],, accessed: 21-May-2017.

(2017) “Theano,” [Online], index.html, accessed: 22-May-2017.

(2017) “Tensorflow,” [Online],, accessed: 22-May-2017.

(2017) “Google Cloud,” [Online],, accessed: 21-May-2017.

(2016) “FloydHub,” [Online],, accessed: 22-May-2017.

T. Chen, R. Xu, Y. He, and X. Wang, “Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN,” Expert Syst. Appl., Vol. 72, pp. 221–230, 2017.

J. D. Prusa and T. M. Khoshgoftaar, “Improving Deep Neural Network Design with New Text Data Representations,” J. Big Data, Vol. 4, No. 7, pp. 1-16, 2017.


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ISSN  : 2550-0554 (online)

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