Mining Unindustrialized Topics Based on User Mention
Keywords:
Change Point Detection, Anomaly scores, MentionsAbstract
Social system is a place where individuals exchange and offer information related to the current events all over the world .This particular behavior of customers made us center on this logic that preparing these substance might commercial us to the extractives the current subject of interest between the users. Applying information bunching procedure like Text-Frequency-based approach over these content might leads us up to the mark in any case there will be some chance of false positives. We propose a likelihood model that can catch both ordinary specifying behavior the other hand of a customer and too the recurrence of customers occurring in their mentions. It too lives up to expectations great indeed the substance of the messages are non-printed information. The test show that the proposed mention-peculiarity based approaches can identify new points at slightest as early as text-peculiarity based approaches, and in some cases much former at the point when the subject is poorly distinguished by the printed substance in the posts.
References
Jun Geng ; Dept. of Electr. & Comput. Eng., Worcester Polytech. Inst., Worcester, MA, USA ; Lifeng Lai, “Bayesian quickest change point detection and localization in sensor networks”, Published in: Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE Date of Conference: 3-5 Dec. 2013 Page(s): 871 – 874.
Fukushima, Y. ; Grad. Sch. of Natural Sci. & Technol., Okayama Univ., Okayama ; Murase, T. ; Fujimaki, R. ; Hirose, S., “Accuracy improvement of multi-stage change-point detection scheme by weighting alerts based on false-positive rate”, Published in: Communications Quality and Reliability, 2009. CQR 2009. IEEE International Workshop Technical Committee on Date of Conference: 12-14 May 2009 Page(s): 1 – 6.
Kyong Joo Oh ; Graduate Sch. of Manage., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea ; Ingoo Han, “An intelligent clustering forecasting system based on change-point detection and artificial neural networks: application to financial economics”, Published in: System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on Date of Conference: 6-6 Jan. 2001.
Ide, T. ; IBM Res., Yamato ; Papadimitriou, S. ; Vlachos, M., “Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors”, Published in: Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on Date of Conference: 28-31 Oct. 2007 Page(s): 523 – 528.
Mishne, G. ; Electr. Eng. Dept., Technion - Israel Inst. of Technol., Haifa, Israel ; Cohen, I., “Multiscale anomaly detection using diffusion maps and saliency score”, Published in: Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on Date of Conference: 4-9 May 2014 Page(s): 2823 – 2827.
Yun Wang ; Cannady, J., “Develop a composite risk score to detect anomaly intrusion”, Published in: SoutheastCon, 2005. Proceedings. IEEE Date of Conference: 8-10 April 2005 Page(s): 445 – 449.
Hailun Lin ; Key Lab. of Network Data Sci. & Technol., Inst. of Comput. Technol., Beijing, China ; Yantao Jia ; Yuanzhuo Wang ; Xiaolong Jin, “Populating knowledge base with collective entity mentions: A graph-based approach”, Published in: Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on Date of Conference: 17-20 Aug. 2014 Page(s): 604 – 611.
Yanping Chen ; Dept. of Comput. Sci. & Technol., Xi'an Jiaotong Univ., Xi'an, China ; Qinghua Zheng ; Ping Chen, “A Boundary Assembling Method for Chinese Entity-Mention Recognition”, Published in: Intelligent Systems, IEEE (Volume:30 , Issue: 6 ) Page(s): 50 – 58.
Zitouni, I. ; IBM T. J. Watson Res. Center, Yorktown Heights, NY ; Xiaoqiang Luo ; Florian, R., “A Cascaded Approach to Mention Detection and Chaining in Arabic”, Published in: Audio, Speech, and Language Processing, IEEE Transactions on (Volume:17 , Issue: 5 ) Page(s): 935 – 944 Date of Publication : July 2009.
Ekbal, A. ; Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Patna, Patna, India ; Saha, S. ; Ravi, K., “Mention detection and classification in bio-chemical domain using Conditional Random Field”, Published in: Emerging Applications of Information Technology (EAIT), 2012 Third International Conference on Date of Conference: Nov. 30 2012-Dec. 1 2012 Page(s): 335 – 338.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
