A Study to Learn Robust and Discriminative Representation to Tackle Cyber bullying Detection

Diddla Mario Praneeth, G.P Madhuri


We build up another content portrayal display in view of a variation of SDA: marginalized stacked denoising autoencoders (mSDA), which receives straight rather than nonlinear projection to quicken preparing and minimizes endless clamor dispersion so as to take in more strong portrayals. We use semantic data to grow mSDA and create Semantic-improved Marginalized Stacked Denoising Autoencoders (smSDA). The semantic data comprises of harassing words. A programmed extraction of tormenting words in view of word embeddings is proposed so that the included human work can be diminished. Amid preparing of smSDA, we endeavor to reproduce bullying highlights from other typical words by finding the idle structure, i.e. connection, amongst tormenting and typical words. The instinct behind this thought is that some harassing messages don't contain bullying words.


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