Abusive messages (flames) can be both a source of frustration and a waste of time for Internet users. This paper describes some approaches to flame recognition, including a prototype system, Smokey. Smokey builds a 47-element feature vector based on the syntax and semantics of each sentence, combining the vectors for the sentences within each message. A training set of 720 messages was used by Quinlan's C4.5 decision-tree generator to determine featurebased rules that were able to correctly categorize 64% of the flames and 98% of the non-flames in a separate test set of 460 messages. Additional techniques for greater accuracy and user customization are also discussed. Introduction Flames are one of the current hazards of on-line communication. While some people enjoy exchanging flames, most users consider these abusive and insulting messages to be a nuisance or even upsetting. I describe Smokey, a prototype system to automatically recognize email flames. Smokey combines natural-langu...

We present experiments on modifying the semantic orientation of the near-synonyms in a text. We analyze a text into an interlingual representation and a set of attitudinal nuances, with particular focus on its near-synonyms. Then we use our text generator to produce a text with the same meaning but changed semantic orientation (more positive or more negative) by replacing, wherever possible, words with nearsynonyms that differ in their expressed attitude. Near-synonyms and attitudinal nuances The choice of a word from among a set of near-synonyms that share the same core meaning but vary in their connotations is one of the ways in which a writer controls the nuances of a text. In many cases, the nuances that differentiate near-synonyms relate to expressed attitude and affect. For