Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder

Authors(1) :-G Netaji

As a side effect of increasingly popular social media, cyberbullying has emerged as a serious problem afflicting children, adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and safe social media environment. In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages. In this paper, we propose a new representation learning method to tackle this problem. Our method named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked denoising autoencoder. The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bullying information and learn a robust and discriminative representation of text. Comprehensive experiments on two public cyberbullying corpora (Twitter and MySpace) are conducted, and the results show that our proposed approaches outperform other baseline text representation learning methods.

Authors and Affiliations

G Netaji
B V C Engineering College, Odalarevu Andhra Pradesh, India

Detection, Cyberbullying,, Social Networking, Denoising

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 851-856
Manuscript Number : CSEIT1723295
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

G Netaji, "Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.851-856, May-June-2017.
Journal URL : http://ijsrcseit.com/CSEIT1723295

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