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 multilevel 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.
REF leveraged a variety of statistical models and rule-based patterns and applied multi-level classification for flame detection.
Offensive language detection using multi-level classification
Advances in Artificial Intelligence
Computer Science