A Machine Learning-based State-of-the-art Approach to Identifying the Person behind an E-mail ID

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Anu B. Nair, R. Umamaheswari, P. Kuppusamy
10.5120/ijca2016908431

Anu B Nair, R Umamaheswari and P Kuppusamy. Article: A Machine Learning-based State-of-the-art Approach to Identifying the Person behind an E-mail ID. International Journal of Computer Applications 136(5):1-4, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Anu B. Nair and R. Umamaheswari and P. Kuppusamy},
	title = {Article: A Machine Learning-based State-of-the-art Approach to Identifying the Person behind an E-mail ID},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {136},
	number = {5},
	pages = {1-4},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

With the growth of internet and related technologies, data available over the web has increased dramatically. As the volume of data increases, the challenge to the computer scientists arises, as knowledge discovery becomes tedious. One of these discovery techniques, which would be widely required soon, would be to identify people and retrieve information about them through social media, via email IDs. In this paper, a state of the art technique is presented, based on Natural Language Processing, to identify details of a person behind an email ID, by scraping social media platforms.

References

  1. Ching-Yung Lin, L. Wu, Zhen Wen, Hanghang Tong, V. Griffiths-Fisher, L. Shi, and D. Lubensky. Social network analysis in enterprise. Proceedings of the IEEE, 100(9):2759– 2776, Sept 2012.
  2. Dong Liu, Li Wang, Jianhua Zheng, Ke Ning, and Liang-Jie Zhang. Influence analysis based expert finding model and its applications in enterprise social network. In Services Computing (SCC), 2013 IEEE International Conference on, pages 368–375, June 2013.
  3. Zheng Lin, Lubin Wang, and Shuhang Guo. Recommendations on social network sites: From link mining perspective. In Management and Service Science, 2009. MASS ’09. International Conference on, pages 1–4, Sept 2009.
  4. Hui-Ju Wu, I-Hsien Ting, and Kai-Yu Wang. Combining social network analysis and web mining techniques to discover interest groups in the blogspace. In Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on, pages 1180–1183, Dec 2009.
  5. P. Fitsilis, V. Gerogiannis, L. Anthopoulos, and A. Kameas. Using social network analysis for software project management. In Current Trends in Information Technology (CTIT), 2009 International Conference on the, pages 1–6, Dec 2009.
  6. Wang Yong-gui and Jia Zhen. Research on semantic web mining. In Computer Design and Applications (ICCDA), 2010 International Conference on, volume 1, pages V1–67–V1–70, June 2010.
  7. T.A. Arunanand, K.A. Abdul Nazeer, M.J. Palakal, and M. Pradhan. A nature-inspired hybrid fuzzy c-means algorithm for better clustering of biological data sets. In Data Science Engineering (ICDSE), 2014 International Conference on, pages 76–82, Aug 2014.
  8. Yaochu Jin and B. Sendhoff. Pareto-based multiobjective machine learning: An overview and case studies. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 38(3):397–415, May 2008.
  9. J. Srivastava. Data mining for social network analysis. In Intelligence and Security Informatics, 2008. ISI 2008. IEEE International Conference on, pages xxxiii–xxxiv, June 2008.
  10. Jason Brownlee. A Tour of Machine Learning Algorithms. http://machinelearningmastery.com/ a-tour-of-machine-learning-algorithms/, 2013. [Online; accessed 19-December-2015].

Keywords

Data Mining, Social Media, Machine Learning