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Comparative Analysis of Human Interaction Pattern Mining Approaches
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Opinion Mining and Sentiment Analysis in Natural Language Processing (NLP) are challenging, as they require deep understanding. Understanding involves methods that could differentiate between the facts of explicit and implicit, regular and irregular, syntactical and semantic language rules. Researches oriented towards Natural Language Processing and Sentiment Analysis have many unresolved problems like co-reference resolution, negation handling, anaphora resolution, named-entity recognition, and word-sense disambiguation. This paper is proposed to develop an Optimized Partial Ancestral Graph (O-PAG) which is capable of mining patterns in human interactions and compare it with an existing tree based pattern mining approach. The experimental results are exposed to number of frequent interactions made and execution time. Results indicate that the overall performance can reach considerable improvements on using O-PAG approach.
Keywords
Frequent Pattern Mining, Sequential Pattern, Enhanced PCA, Enhanced ABC, Pattern Mining.
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- Z.W. Yu, Z.Y. Yu, H. Aoyama, M. Ozeki and Y. Nakamura, “Capture, Recognition, and Visualization of Human Semantic Interactions in Meetings”, Proceedings of 8th IEEE International Conference on Pervasive Computing and Communications, pp. 107-115, 2010.
- S. Uma and J. Suguna “Tree-Based Weighted Interesting Pattern Mining approach for Human Interaction Pattern Discovery”, International Review on Computers and Softwares, Vol. 8, No. 11, pp. 2570-2575, 2013.
- Henrique Siqueira and Flavia Barros, “A Feature Extraction Process for Sentiment Analysis of Opinions on Services”, Proceedings of International Workshop on Web and Text Intelligence, pp. 1-10, 2010.
- Efthymios Kouloumpis, Theresa Wilson and Johanna D. Moore, “Twitter Sentiment Analysis: The Good the Bad and the OMG!”, Proceedings of 5th International Conference on Weblogs and Social Media, pp. 538-541, 2011.
- Charles J. Fillmore and Collin Baker, “A Frames Approach to Semantic Analysis”, Oxford University Press, 2018.
- Charu C Aggarwal and Cheng Xiang Zhai, “Mining Text Data”, Springer, 2012.
- Li Yang, Xinyu Geng and Haode Liao, “A Web Sentiment Analysis Method on Fuzzy Clustering for Mobile Social Media Users”, Journal on Wireless Communications and Networking, Vol. 128, pp. 1-16, 2016.
- Christian Becker and Zhiwen Yu, “Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings”, Proceedings of IEEE International of Conf. Knowledge Discovery and Data Mining, pp. 107-115, 2012.
- David G. Kleinbaum, L.L. Kupper, A. Nizam and K.E. Muller, “Applied Regression Analysis and Multivariable Methods”, Duxbury Press, 2007.
- S. Uma. And J. Suguna, “Human Interaction Pattern Mining using Enhanced Principal Component Analysis”, International Journal of Informative and Futuristic Research, Vol. 2, No. 7, pp. 2279-2289, 2015.
- W. Gao, S. Liu and L. Huang, “A Global best Artificial Bee Colony Algorithm for Global Optimization”, Journal of Computational and Applied Mathematics, Vol. 236, No. 11, pp. 2741-2753, 2012.
- Dervis Karaboga and Bahriye Basturk, “Artificial Bee Colony Optimization Algorithm for Solving Constrained Optimization Problems”, Proceedings of International Fuzzy Systems Associations, pp. 789-798, 2007.
- S. Uma. and J. Suguna, “Human Interaction Pattern Mining using Enhanced Artificial Bee Colony Algorithm”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, No. 9, pp. 2320-9798, 2015.
- S. Uma and J. Suguna, “Temporal Semantic Analysis Based Human Interaction Pattern Mining using Partial Ancestral Graph”, Research Journal of Applied Sciences, Engineering and Technology, Vol. 8, No. 12, pp. 1487-1491, 2014.
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