Offensive language detection using multi-level classification

Authors:
Amir H. Razavi;Diana Inkpen;Sasha Uritsky;Stan Matwin
Affiliations:
School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, ON, Canada;School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, ON, Canada;Natural Semantic Modules co., Toronto, ON;,School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, ON, Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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Abstract

Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons An automatic discriminative software with a sensitivity parameter for flame or abusive language detection would be a useful tool Although a human could recognize these sorts of useless annoying texts among the useful ones, it is not an easy task for computer programs In this paper, we describe an automatic flame detection method which extracts features at different conceptual levels and applies multi-level classification for flame detection While the system is taking advantage of a variety of statistical models and rule-based patterns, there is an auxiliary weighted pattern repository which improves accuracy by matching the text to its graded entries.