Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Representing emotional momentum within expressive internet communication
IMSA'06 Proceedings of the 24th IASTED international conference on Internet and multimedia systems and applications
Spatial Presence and Emotions during Video Game Playing: Does It Matter with Whom You Play?
Presence: Teleoperators and Virtual Environments
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
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Identifying expressions of emotion in text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Identifying emotions, intentions, and attitudes in text using a game with a purpose
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Modeling emotions and other motivations in synthetic agents
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Identifying sarcasm in Twitter: a closer look
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Using a heterogeneous dataset for emotion analysis in text
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Emotion estimation and reasoning based on affective textual interaction
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Emotional sequencing and development in fairy tales
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Tracking sentiment in mail: how genders differ on emotional axes
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Automated mark up of affective information in english texts
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
From once upon a time to happily ever after: Tracking emotions in mail and books
Decision Support Systems
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
Distant supervision for emotion classification with discrete binary values
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Identifying purpose behind electoral tweets
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
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Detecting emotions in microblogs and social media posts has applications for industry, health, and security. However, there exists no microblog corpus with instances labeled for emotions for developing supervised systems. In this paper, we describe how we created such a corpus from Twitter posts using emotion-word hashtags. We conduct experiments to show that the self-labeled hashtag annotations are consistent and match with the annotations of trained judges. We also show how the Twitter emotion corpus can be used to improve emotion classification accuracy in a different domain. Finally, we extract a word-emotion association lexicon from this Twitter corpus, and show that it leads to significantly better results than the manually crafted WordNet Affect lexicon in an emotion classification task.