ROLE OF ARTIFICIAL NEURAL NETWORK IN OPINION CLASSIFICATION

Authors

  • Dr. Helen Josephine V L Associate Professor, Department of Computer Applications, CMR Institute of Technology, Bengaluru Author
  • Gomathi Thiyagarajan Assistant Professor, Department of Computer Applications, CMR Institute of Technology, Bengaluru Author
  • Dr. V. S. Anita Sofia Associates Professor, Department of Computer Technology, Sri Krishna Arts and Science College, Coimbatore Author

DOI:

https://doi.org/10.61841/peh8p689

Keywords:

Classification, Machine Learning, Neural Network, Opinion Mining

Abstract

In the present world, internet users are expanding rapidly, and consumers are more involved in searching for and selecting the best product. E-commerce organizations also spend a lot of time, effort, and cost to investigate the comments and feedback about their products. This scrutiny would assist the corporations to enhance their services and products at low cost, which in turn would facilitate them to extend and flourish in the industry. Extracting knowledge from vast unstructured data, which is in the form of customer reviews, finds it very difficult to analyze and evaluate. This research paper analyzes the role of neural networks in opinion mining classification and proposes a gain ratio feedforward neural network (GR_FFNN) algorithm to improve classification accuracy. This paper also compares the GR_FFNN algorithm with the existing neural network classifier. 

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References

1. X.Hu and B.Wu, 2009, Classification and Summarization of Pros and Cons for Customer Reviews, Web Intelligence and Intelligent Agent Technologies, Vol. 3, pp. 73–76.

2. Go A., Bhayani R., and Huang L. (2009), Twitter sentiment classification using distant supervision, CS224N Natural Language Processing in Spring, pp. 1–6.

3. Lina L. Dhande and Dr. Prof. Girish K. Patnaik, "Analyzing Sentiment of Movie Review Data Using Naive Bayes Neural Classifier," International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 3, Issue 4, July-August 2014, pp. 313-320, ISSN 2278-6856.

4. Cambria, E., Mazzocco, T., & Hussain, A. (2013), Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining, Biologically Inspired Cognitive Architectures, vol. 4, pp. 41-53.

5. V. Dhanalakshmi, Dhivya Bino, and A. M. Saravanan (2016), Opinion mining from student feedback data using supervised learning algorithms, 3rd MEC International Conference on Big Data and Smart City (ICBDSC)

6. Moraes, R., Valiati, J. F., & GaviãO Neto, W. P. (2013), Document-level sentiment classification: An empirical comparison between SVM and ANN, Expert Systems with Applications, vol. 40, no. 2, pp. 621-633.

7. Ghiassi, M., Skinner, J., & Zimbrqa, D. (2013), Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural networks, Expert Systems with Applications: An International Journal, vol. 40, no. 16, pp. 6266-6282

8. Anuj Sharma Shubhamoy Dey (2012), A Document-Level Sentiment Analysis Approach Using Artificial Neural Networks and Sentiment Lexicons, ACM SIGAPP Applied Computing Review December 2012.

9. N. Mohd Nawi, R.S. Ransing, and M.S. Ransing (2008), An Improved Conjugate Gradient-Based Learning Algorithm for Back Propagation Neural Networks, International Journal of Information and Mathematical Sciences, Vol. 4, no. 1.

10. Ozan Irsoy and Claire Cardie (2014), Opinion Mining with Deep Recurrent Neural Networks, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 720–728.

11. Huawang Shi (2009), Evolving Artificial Neural Networks Using GA and Momentum, Second International Symposium on Electronic Commerce and Security, Nanchang, pp. 475-478.

12. Norhamreeza Abdul Hamid, Nazri Mohd Nawi, Rozaida Ghazali, and Mohd Najib Mohd Salleh (2011) Solving Local Minima Problem in Back Propagation Algorithm Using Adaptive Gain, Adaptive Momentum, and Adaptive Learning Rate on Classification Problems, International Conference Mathematical and Computational Biology, pp. 448–455.

13. Asha Gowda Karegowda, M.A. Jayaram, and A.S. Manjunath, “Feature Feature Subset Selection Problem using Wrapper Approach in Supervised Learning." International Journal on Computer Applications (IJCA), Volume 1, 2010.

14. Gongde Guo, Daniel Neagu1, and Mark Cronin (2001), A Study on Feature Selection for Toxicity Prediction EPSRC Project: PYTHIA: Predictive Toxicology Knowledge Representation and Processing Tool based on a hybrid intelligent systems approach.

15. Rosenblatt, Frant (1958), The Perceptron: A probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory, Psychological Review, vol. 65, no. 6, pp. 386-408

16. Fausett, Laurene. (1994), Fundamentals of neural networks: Architectures, algorithms, and applications. New Jersey: Prentice Hall.

17. Jack V.Tu Tu et al. (1996), Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, Journal of Clinical Epidemiology, vol. 49, no. 11, pp. 1225-1231

18. Jiang Tengjiao, Zhong Minjuan, Liao Shumei, and Luo Siwen (2016), Mining Opinion Word (2016), Mining Opinion Word from Customer Review, International Journal of Database and Theory and Application, vol. 9, no. 2, pp. 129-136.

19. Elman, Jeffrey L. (1990). Finding structure in time. Cognitive Science, vol. 14, pp. 179-211.

20. Isik Yilmaz, Nazan Yalcin Erik, and Oguz Kaynar (2011), Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals, Scientific Research and Essays, vol. 5, no. 16, pp. 2242-2249.

21. Huang, Z., D.D. Zeng, and H. Chen (2007), Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems. Management Science, vol. 53, no. 7, pp. 1146-1164.

22. Shikha Chourasia (2013), Survey paper on improved methods of ID3 decision tree classification, International Journal of Scientific and Research Publications, vol. 3, no. 12

23. H. Josephine, S. Duraisamy, "Novel pre-processing framework to improve classification accuracy in opinion mining," International journal of computing, vol. 17, no. 4, pp. 234-242, 2018.

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Published

30.04.2020

How to Cite

Josephine V L, H., Thiyagarajan, G., & Anita Sofia, V. S. (2020). ROLE OF ARTIFICIAL NEURAL NETWORK IN OPINION CLASSIFICATION. International Journal of Psychosocial Rehabilitation, 24(2), 4407-4416. https://doi.org/10.61841/peh8p689