Abstract
Detecting trending topics is perfect to summarize information getting from social media. To extract what topic is becoming hot on online media is one of the challenges. As we considering social media so social services are opportunity for spamming which greatly affect on value of real time search. Therefore the next task is to control spamming from social networking sites. For completing these challenges different concepts of data mining will be used. For now whatever work has been done is narrated below like spam control using natural language processing for preprocessing and clustering. One account has been created for making it real.
References
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13. Cristina Rădulescu , Mihaela Dinsoreanu , Rodica Potolea ,“Identification of Spam Comments using Natural Language Processing Techniques”, IEEE 2014
14. Zengcai Su , Hua Xu;_ , Dongwen Zhang and Yunfeng Xu ,“ Chinese Sentiment Classification Using A Neural Network Tool - Word2vec”, InMultisensor Fusion and Information Integration for Intelligent Systems(MFI), International Conference on, pages 1–6. IEEE, 2014
15. https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_natural_language_processing.htm
2. Cristina Radulescu, Mihaela Dinsoreanu, and Rodica Potolea, “Identification of spam comments using natural language processing techniques”,In Intelligent Computer Communication and Processing (ICCP), 2014 IEEE International Conference on, pages 29–35. IEEE, 2014.
3. M. Cataldi, L. Di Caro and C. Schifanella, “Emerging topic detection on Twitter based on temporal and social terms evaluation,” in Proc. MDMKDD: 10th Int. Workshop Multimedia Data Mining, New York, NY, USA, 2010, pp. 4:1–4:10, ACM.
4. Sayyadi, M. Hurst and A.Maykov, “Event detection and tracking in social streams,” in ICWSM, E. Adar, M. Hurst, T. Finin, N. S. Glance, N. Nicolov, and B. L. Tseng, Eds. Palo Alto, CA, USA:
AAAI Press, 2009.
5. J. Leskovec, L. Backstrom, and J. Kleinberg, “Meme-tracking and the dynamics of the news cycle,” in Proc. KDD: 15th ACM Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 2009, pp. 497–506.
6. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, Mar. 2003
7. Joseph Lilleberg, Yun Zhu, and Yanqing Zhang,“Support vector machines and word2vec for text classification with semantic features”.,In Cognitive Informatics & Cognitive Computing (ICCI* CC), IEEE 14th International Conference on, pages 136–140, 2015.
8. Bai Xue, Chen Fu, and Zhan Shaobin,“A study on sentiment computing and classification of sina weibo with word2vec.” In Big Data (BigData Congress), IEEE International Congress on, pages 358–363 IEEE, 2014.
9. Huiyu Wang, Kai Lei, and Kuai Xu, “Profiling the followers of the most influential and verified users on sina weibo.”, In Communications (ICC), IEEE International Conference on, pages 1158–1163. IEEE, 2015.
10. Ala’ M. Al-Zoub, Ja’far Alqatawna, Hossam Faris,“Spam Profile Detection in Social Networks Based on Public Features”, 8th International Conference on Information and Communication Systems (ICICS),2017.
11. Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon ,“ What is Twitter, a Social Network or a News Media?”, In Proceedings of the 19th international conference on World wide web, pages 591–600. ACM,2010
12. Rohit Giyanani, Mukti Desai,“Spam Detection using Natural Language Processing”,IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 5, Ver. IV (Sep – Oct. 2014), PP 116-119
13. Cristina Rădulescu , Mihaela Dinsoreanu , Rodica Potolea ,“Identification of Spam Comments using Natural Language Processing Techniques”, IEEE 2014
14. Zengcai Su , Hua Xu;_ , Dongwen Zhang and Yunfeng Xu ,“ Chinese Sentiment Classification Using A Neural Network Tool - Word2vec”, InMultisensor Fusion and Information Integration for Intelligent Systems(MFI), International Conference on, pages 1–6. IEEE, 2014
15. https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_natural_language_processing.htm
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