Binary and Continuous Feature Engineering Analysis on Twitter Data Stream for Classification of Spam Messages

Author:

Kiliroor Cinu C.,Valliyammai C.

Publisher

Springer Singapore

Reference15 articles.

1. Bing, X., Mengjie, Z., Will, N., Browne, Xin, Y.: A survey on evolutionary computation approaches to features selection. IEEE Trans. Evol. Comput. 20, 606–626 (2016)

2. Guanjun, L., Nan, S., Surya, N., Jun, Z., Yang, X., Houcine, H.: Statistical twitter spam detection demystified: performance, stability and scalability. IEEE Access Big Data Anal. Internet Things Cyber-Phys. Syst. 5, 11142–11154 (2017)

3. Issa, A., Gan, K.H.: Term weighting scheme for short-text classification: Twitter corpuses. Int. J. Neural Comput. Appl. (2018) (Springer)

4. Jaishree, R., Allen, S., Irudayaraj, A., Tzacheva, A.A: Action rules for sentiment analysis on Twitter data using spark. In: IEEE International Conference on Data Mining Workshops, pp. 51–60 (2017)

5. Lalitha, L.A., Hulipalled, V.R., Venugopal, K.R.: Spamming the mainstream: a survey on trending Twitter spam detection techniques. In: IEEE International Conference on Smart Technology for Smart Nation, pp. 444–448 (2017)

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