WordNet: a lexical database for English
Communications of the ACM
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Emotion Classification Using Web Blog Corpora
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Let's Gang Up on Cyberbullying
Computer
A Normative Agent System to Prevent Cyberbullying
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Concept labeling: building text classifiers with minimal supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning from bullying traces in social media
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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Bullying is a serious national health issue among adolescents. Social media offers a new opportunity to study bullying in both physical and cyber worlds. Sentiment analysis has the potential to identify victims who pose high risk to themselves or others, and to enhance the scientific understanding of bullying overall. We identify seven emotions common in bullying. While some of the emotions are well-studied before, others are non-standard in the sentiment analysis literature. We propose a fast training procedure to recognize these emotions without explicitly producing a conventional labeled training dataset. We apply our procedure to social media posts on bullying and discuss our findings.