Identifying Hate Speech and Abusive Content in Social Media Data Based On Machine Learning Based Techniques

Due to the emergence of social media becoming a common platform for sharing of views and day to day activities, more researches are aimed at extracting data out of social media data mining, one subset of which is text mining of social media data such as twitter, facebook, Whatsapp etc. With the advent of social media, the exchange of data has become extremely easy. However, this sometimes may lead to easy share of radical content which needs to filtered out very quickly to avoid losses, damages and possible scenarios of violence. Machine learning based approaches are indispensable for the detection and classification of the radical and possible terrorist activity conducive content due to the size and complexity of data being bombarded on social media platforms. Due the enormous amount of data available with the websites, they become a natural choice for text mining and/or opinion mining. This paper presents the necessity for text cum opinion mining based Sentiment analysis and its various associated techniques to filter out radical content. It is expected that the paper will pave a path for future researchers to carry forward their research in a direction which best suits their application