Comparative Analysis of Human Interaction Pattern Mining Approaches
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.
Z.W. Yu, Z.Y. Yu, H. Aoyama, M. Ozeki, and Y. Nakamura, “Capture, Recognition, and Visualization of Human Semantic Interactions in Meetings,” Proc. Eighth IEEE Int’l Conf. Pervasive Computing and Comm. (PerCom ’10), pp. 107-115, Mar.-Apr. 2010.
S.Uma and J.Suguna “Tree-Based Weighted Interesting Pattern Mining approach for Human Interaction Pattern Discovery” IRECOS ,(vol.8 n.11), November 2013
Siqueira, Henrique, and Flavia Barros. "A feature extraction process for sentiment analysis of opinions on services." Proceedings of International Workshop on Web and Text Intelligence. 2010.
Kouloumpis, Efthymios, Theresa Wilson, and Johanna D. Moore, “Twitter Sentiment Analysis: The Good the Bad and the OMG!”, 2011.
Fillmore, Charles J., and Collin Baker, “A Frames Approach to Semantic Analysis”, Oxford University Press, 2018.
Aggarwal, Charu C., and ChengXiangZhai, “Mining Text Data”, ISBN- 978146143227, 2012.
Li Yang, XinyuGeng and Haode Liao, “A web sentiment analysis method on fuzzy clustering for mobile social media users”, Journal on Wireless Communications and Networking, 2016.
Christian Becker and Zhiwen Yu, “Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings”,’ Proc. Eighth IEEE Int’l Conf. Knowledge Discovery and Data Mining, pp 107-115,2012
Kleinbaum, David G., et al., “Applied Regression Analysis and Multivariable methods”, 3rd edition, (Pacific Grove, Ca.: Brooks/Cole Publishing Company, 1998).
Uma. S, Suguna.J, “Human Interaction Pattern Mining Using Enhanced Principal Component Analysis”, International Journal of Informative & Futuristic Research, ISSN: 2347-1697, vol.2, no.7, pp. 2279-2289, March 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.
DervisKaraboga ,BahriyeBasturk., “Artificial Bee colony Optimization Algorithm for solving Constrained Optimization Problems”, IFSA 2007, LNAI 4529, pp.789-798, 2007.
Uma. S, Suguna.J, “Human Interaction Pattern Mining Using Enhanced Artificial Bee Colony Algorithm”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN: 2320-9801, vol.3, no.9, pp. 2320-9798, September 2015.
S. Uma. S, J. Suguna, “Temporal Semantic Analysis Based Human Interaction Pattern Mining using Partial Ancestral Graph”, Research Journal of Applied Sciences, Engineering and Technology, 8(12), pg 1487-1491 (2014)
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