Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
Lexical and Discourse Analysis of Online Chat Dialog
ICSC '07 Proceedings of the International Conference on Semantic Computing
Toward Spotting the Pedophile Telling victim from predator in text chats
ICSC '07 Proceedings of the International Conference on Semantic Computing
Automatically profiling the author of an anonymous text
Communications of the ACM - Inspiring Women in Computing
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Improving word sense disambiguation in lexical chaining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Predicting age and gender in online social networks
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Learning to Identify Internet Sexual Predation
International Journal of Electronic Commerce
Automatic emotion classification for interpersonal communication
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Modelling fixated discourse in chats with cyberpedophiles
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
Towards detection of child sexual abuse media: categorization of the associated filenames
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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According to previous work on pedophile psychology and cyberpedophilia, sentiments and emotions in texts could be a good clue to detect online sexual predation. In this paper, we have suggested a list of high-level features, including sentiment and emotion based ones, for detection of online sexual predation. In particular, since pedophiles are known to be emotionally unstable, we were interested in investigating if emotion-based features could help in their detection. We have used a corpus of predators' chats with pseudo-victims downloaded from www.perverted-justice.com and two negative datasets of different nature: cybersex logs available online and the NPS chat corpus. Naive Bayes classification based on the proposed features achieves accuracies of up to 94% while baseline systems of word and character n-grams can only reach up to 72%.