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Abstract
One of the newest areas of research is in data mining and text mining is automatic discovery of knowledge from semi-structured text. an important application of data mining in the classification texts.neural networks have emerged as a powerful tool in the classification of the content of texts and a promising alternative to conventional methods of classification. in this study, by combining and improving the regulatory procedure parameters weighted binary artificial neural network model, we will provide an algorithm to classify content. This algorithm content of the texts of the Persian texts will be based on the polarity of emotion suitable performance and high accuracy. The proposed algorithm is implemented using the simulation software MATLAB and evaluated collected over 1200 comments in Farsi in the real environment. The results above show that the proposed algorithm is a neural network classification accuracy of 96% negative polarity and positive polarity sentences based on document content.
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References
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References
Ali Mardani, S., & Aghaee, A. 2015. Monitoring methods for data-mining algorithms in the Persian language using dictionaries and SVM. Tehran University, 1-18.
Bayat, M., Hosseini Khozani, S., & Gaplh, M. 2014. Automatic sorting Persian texts using neural networks, the first National Conference on Soft Computing and I T 1-6.
Cassinelli, A., & Chen, C.-W. 2009. Sentiment Categorization with Machine Learning Techniques. CS 224N Final Project, 1-12.
Dehbashyan, M., & Zahiri, S. 1389. Education MLP neural network to classify data by GSA. College of Electrical Engineering and Computer Engineering, 267-274.
Dhande, L., & Patnaik, G. 2014. Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 313-320.
Fayyad, U., & Piatetsky-Shapiro, G. 1996. Knowledge discovery and data mining: Towards a unifying framework. In Knowledge Discovery and Data Mining. 82-88.
Han, J. 2006. Data Mining: Concepts and Techniques. University of Illinois at Urbana-Champaign-ELSEVIER.
Karanasou, M., Doulkeridis, C., & Halkidi, M. 2015. An SVM-based Approach for Sentiment Analysis of Figurative. Proceedings of the 9th International Workshop on Semantic Evaluation, 709-713.
Karanikas, H., Tjortjis, C., & Theodoulidis, B. 2012. An Approach to Text Mining using Information Extraction. Centre for Research in Information Management Department of Computation, UMIST, P.O. Box 88,Manchester, M60 1QD, UK, 1-14.
Mahnaj, M. 2002. Computational Intelligence. Center Amir Kabir University Press.
Mirdamadi, M., Zare Bideki, A., & Rezaeian, M. 1392. Expressions Persian texts segmentation using neural networks. College of Electrical Engineering and Computer Engineering, B - Computer Engineering, 76-84.
Myrzavnd, M., & Naderi. 2014. Advanced database. University Islamic Azad University research.
Rahate, R., & Emmanuel, M. 2013. Feature Selection for Sentiment Analysis by using SVM. International Journal of Computer Applications, 24-32.
Saraswathi, K., & Tamilarasi, A. 2014. Investigation of support vector machine classifier for opinion mining. Journal of Theoretical and Applied Information Technology, 291-296.