WordNet: a lexical database for English
Communications of the ACM
Color as a determined communication
IBM Systems Journal
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
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
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
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
Colourful language: measuring word-colour associations
CMCL '11 Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
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
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Existing concept-color-emotion lexicons limit themselves to small sets of basic emotions and colors, which cannot capture the rich pallet of color terms that humans use in communication. In this paper we begin to address this problem by building a novel, color-emotion-concept association lexicon via crowdsourcing. This lexicon, which we call CLEX, has over 2,300 color terms, over 3,000 affect terms and almost 2,000 concepts. We investigate the relation between color and concept, and color and emotion, reinforcing results from previous studies, as well as discovering new associations. We also investigate cross-cultural differences in color-emotion associations between US and India-based annotators.