International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Deep Learning Model for Cyber Bullying Detection in the Text Representation for Social Media

Author : Tintu Jose ¹

Date of Publication :7th July 2016

Abstract: The social media plays major role in introducing the innovative learning and teaching using the social media for making the clear understanding of the theme in both the beginning stage of research on web 2.0 technologies, which represented by wikis and blogs The most of the research on social networking sites such as Twitter and Face book are utilized for making the analysis of clear theme of the web. In this study, one of the important issues is discriminative and robust in the text representation of understanding in the messages. In this paper, we propose a deep representation learning mechanism to handle such issues in text representation. Our scheme, is termed as Semantic-Enhanced Marginalized De-noising Auto-Encoder (SMSDA ) is enhanced from the familiar deep learning model stacked of the de-noising auto encoder. In our proposed method to detect the cyber bulling in the text representation, we include sparsity constraints and semantic dropout noise, where it improves to reconstruct the original data from the domain knowledge with the help of word embedding method. Our proposed scheme is capacity to feat the hidden feature structure of bullying data and learns a more discriminative and robust in the text representation.

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