Making large-scale support vector machine learning practical
Advances in kernel methods
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Offensive language detection using multi-level classification
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Content matters: a study of hate groups detection based on social networks analysis and web mining
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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We present an approach to detecting hate speech in online text, where hate speech is defined as abusive speech targeting specific group characteristics, such as ethnic origin, religion, gender, or sexual orientation. While hate speech against any group may exhibit some common characteristics, we have observed that hatred against each different group is typically characterized by the use of a small set of high frequency stereotypical words; however, such words may be used in either a positive or a negative sense, making our task similar to that of words sense disambiguation. In this paper we describe our definition of hate speech, the collection and annotation of our hate speech corpus, and a mechanism for detecting some commonly used methods of evading common "dirty word" filters. We describe pilot classification experiments in which we classify anti-semitic speech reaching an accuracy 94%, precision of 68% and recall at 60%, for an F1 measure of. 6375.