A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Gender-Preferential Text Mining of E-mail Discourse
ACSAC '02 Proceedings of the 18th Annual Computer Security Applications Conference
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Emotions from text: machine learning for text-based emotion prediction
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
Using natural language processing to classify suicide notes
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Clustering semantic spaces of suicide notes and newsgroups articles
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Data mining emotion in social network communication: Gender differences in MySpace
Journal of the American Society for Information Science and Technology
Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Emotion analysis using latent affective folding and embedding
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Inferring gender of movie reviewers: exploiting writing style, content and metadata
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Even the abstract have colour: consensus in word-colour associations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
From once upon a time to happily ever after: tracking emotions in novels and fairy tales
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
From once upon a time to happily ever after: tracking emotions in novels and fairy tales
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
In the mood for affective search with web stereotypes
Proceedings of the 21st international conference companion on World Wide Web
Portable features for classifying emotional text
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
A context-sensitive, multi-faceted model of lexico-conceptual affect
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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With the widespread use of email, we now have access to unprecedented amounts of text that we ourselves have written. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in many types of mail. We create a large word--emotion association lexicon by crowdsourcing, and use it to compare emotions in love letters, hate mail, and suicide notes. We show that there are marked differences across genders in how they use emotion words in work-place email. For example, women use many words from the joy--sadness axis, whereas men prefer terms from the fear--trust axis. Finally, we show visualizations that can help people track emotions in their emails.