Application of neural networks to the analysis of consumer opinions
Roman Mysan
roman.mysan@pollub.edu.plLublin University of Technology (Ukraine)
Ivan Loichuk
Lublin University of Technology (Ukraine)
Małgorzata Plechawska-Wójcik
Lublin University of Technology (Poland)
Abstract
This paper presents an analysis of the possibilities of using neural networks to classify text data in the form of comments. Moreover, results of research of two neural network optimization methods: Adam and Gradient are presented. The aim of the work is to conduct research on the behavior of the neural network depending on the change of parameters and the amount of data used to teach the neural network. To achieve the goal, a test application was created. It uses a neural network to display the overall assessment of the accommodation facility based on the added user feedback.
Keywords:
neural network; TensorFlow; artificial intelligenceReferences
[1] M. Fábio, M. F. Alan, Neural network programming with Java, Packt Publishing, 2016
[2] P. D. Kingma, J. Lei Ba: Adam: a method for stochastic optimization, Published as a conference paper at ICLR 2015
[3] B. Yoshua, Practical Recommendations for Gradient-Based Training of Deep Architectures, Version 2, Sept. 16th, 2012
[4] K. Aurangzeb, B. Baharum, L. Lam Hong*, K. Khairullah, A Review of Machine Learning Algorithms for Text-Documents Classification, Journal of advances in information technology, vol. 1, no. 1, february 2010
[5] Y. Hongsuk, J. HeeJin, B. Sanghoon, Deep Neural Networks for traffic flow prediction, Published in: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)
[6] S. Ruder, An overview of gradient descent optimization algorithms∗, Insight Centre for Data Analytics, NUI Galway Aylien Ltd., Dublin, 15 Jun 2017
[7] M. Kaut, S. W. Wallace, Evaluation of scenario-generation methods for stochastic programming, March 2007
[8] https://www.wired.com/2016/06/how-google-is-remaking-itself-as-a-machine-learning-first-company/ [22.06.2019]
[9] P. Lula, Text-mining jaką narzędzie pozyskiwania informacji z dokumentów tekstowych, Akademia Ekonomiczna w Krakowie, Katefra Informatyki, 2005
[10] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification, 6 Jul 2016
[11] Z. Min-Ling, Z. Zhi-Hua, Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization, 28 August 2006
[12] S. Fabrizio, Machine learning in automated text categorization, March 2002
[2] P. D. Kingma, J. Lei Ba: Adam: a method for stochastic optimization, Published as a conference paper at ICLR 2015
[3] B. Yoshua, Practical Recommendations for Gradient-Based Training of Deep Architectures, Version 2, Sept. 16th, 2012
[4] K. Aurangzeb, B. Baharum, L. Lam Hong*, K. Khairullah, A Review of Machine Learning Algorithms for Text-Documents Classification, Journal of advances in information technology, vol. 1, no. 1, february 2010
[5] Y. Hongsuk, J. HeeJin, B. Sanghoon, Deep Neural Networks for traffic flow prediction, Published in: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)
[6] S. Ruder, An overview of gradient descent optimization algorithms∗, Insight Centre for Data Analytics, NUI Galway Aylien Ltd., Dublin, 15 Jun 2017
[7] M. Kaut, S. W. Wallace, Evaluation of scenario-generation methods for stochastic programming, March 2007
[8] https://www.wired.com/2016/06/how-google-is-remaking-itself-as-a-machine-learning-first-company/ [22.06.2019]
[9] P. Lula, Text-mining jaką narzędzie pozyskiwania informacji z dokumentów tekstowych, Akademia Ekonomiczna w Krakowie, Katefra Informatyki, 2005
[10] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification, 6 Jul 2016
[11] Z. Min-Ling, Z. Zhi-Hua, Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization, 28 August 2006
[12] S. Fabrizio, Machine learning in automated text categorization, March 2002
Mysan, R., Loichuk, I., & Plechawska-Wójcik, M. (2019). Application of neural networks to the analysis of consumer opinions . Journal of Computer Sciences Institute, 13, 310–314. https://doi.org/10.35784/jcsi.1325
Authors
Ivan LoichukLublin University of Technology Ukraine
Authors
Małgorzata Plechawska-WójcikLublin University of Technology Poland
Statistics
Abstract views: 228PDF downloads: 216
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
