Cyber Bullying Detection on Social Media based on Denoising Auto-Encoder

Authors

  • Fatima R CSE Dept., KBNCE, Kalaburagi
  • Khadija U M. Tech Student KBNCE, Kalaburagi

DOI:

https://doi.org/10.26438/ijcse/v6i9.183187

Keywords:

NLP, cyberbullying, Social Network, Mining, collaboration

Abstract

As a signal of more and more distinguished on-line networking, cyberbullying has developed as a big issue harassing kids, adolescents and vernal grown-ups. Machine learning procedures build programmed recognition of harassing messages in web-based social networking doable, and this might build a solid and safe web-based social networking condition. During this important analysis zone, one basic issue is powerful and discriminative numerical portrayal learning of instant messages. During this paper, we tend to propose another portrayal learning strategy to handle this issue. Our technique named SemanticEnhanced Marginalized Denoising Auto-Encoder (smSDA) is created by means that of linguistics enlargement of the notable profound learning model stacked denoising autoencoder. The linguistics enlargement includes of linguistics dropout commotion and meagerness limitations, wherever the linguistics dropout clamor is planned in sight of area learning and therefore the word inserting system. Our planned strategy will misuse the hid part structure of tormenting knowledge and soak up a full of life and discriminative portrayal of content.

References

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Published

2018-09-30
CITATION
DOI: 10.26438/ijcse/v6i9.183187
Published: 2018-09-30

How to Cite

[1]
R. Fatima and U. Khadija, “Cyber Bullying Detection on Social Media based on Denoising Auto-Encoder”, Int. J. Comp. Sci. Eng., vol. 6, no. 9, pp. 183–187, Sep. 2018.

Issue

Vol. 6 No. 9 (2018): IJCSE September Editor

Section

Research Article

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