Automatic text categorisation of racist webpages
Published 1 January 2004Open access
Edel Greevy
Citations10
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TL;DR
This thesis uses a machine learning method called Support Vector Machines to automatically learn features of racism in order to be capable of making a decision about the target class of unseen documents, and shows that it is possible to develop automatic categorisation of web pages, based on these approaches.
Abstract
Automatic Text Categorisation (TC) involves the assignment of one or more predefined categories to text documents in order that they can be effectively managed. In this thesis we examine the possibility of applying automatic text categorisation to the problem of categorising texts (web pages) based on whether or not they are racist. \n \nTC has proven successful for topic-based problems such as news story categorisation. However, the problem of detecting racism is dissimilar to topic-based problems in that lexical items present in racist documents can also appear in anti-racist documents or indeed potentially any document. The mere presence of a potentially racist term does not necessarily mean the document is racist. The difficulty is finding what discerns racist documents from non-racist. \n \nWe use a machine learning method called Support Vector Machines (SVM) to automatically learn features of racism in order to be capable of making a decision about the target class of unseen documents. We examine various representations within an SVM so as to identify the most effective method for handling this problem. Our work shows that it is possible to develop automatic categorisation of web pages, based on these approaches
Keywords
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