Development of Novel Classifying System to Identify the Right Sense of Textual Conversation in Social Networks using Deep Convolution Neural Network

Abstract

Social media has paved a new way for communication and interacting with others. The use of social media differs according to the socio-cultural, demographic and psychological aspects of individuals. People chat, share ideas and visual material, andfeelthattheysatisfytheirneedsofbelongingalongwiththegroupsthey have joined. Social networks is not only a area of freedom where persons express themselves openly or furtively, but also an area where several ways of violence emerge or even a means used for some aspects of violence.. Thepresent research throwslightonafewoftheregularandtrendymethodsofabuseandrisksfacedbytheusersofsocial media. Develop a system to identify abusing text by an individual on a people/ group based on commonlanguage,race,sexualpreferences,religion,ornationality. We examine a new model from machine learning, namely deep machine learning by probing design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper-parameter settings in identifying the negative aspects in social media. Deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge. An application of the proposed method demonstrates excellent results with low false alarm rates for Twitter data.