Emotion classification using massive examples extracted from the web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A text-driven rule-based system for emotion cause detection
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
NewsViz: emotional visualization of news stories
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Design and implementation of GEmA: A generic emotional agent
Expert Systems with Applications: An International Journal
Imhotep: an approach to user and device conscious mobile applications
Personal and Ubiquitous Computing
Sentiment analysis of news titles the role of entities and a new affective lexicon
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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This paper describes a character-based system called "Emotion Sensitive News Agent" (ESNA). ESNA is been developed as a news aggregator to fetch news from different news sources chosen by a user, and to categorize the themes of the news into eight emotion types. A small user study indicates that the system is conceived as intelligent and interesting as an affective interface. ESNA exemplifies a recent research agenda that aims at recognizing affective information conveyed through texts. News is an interesting application domain where user may have marked attitudes to certain events or entities reported about. Different approaches have already been employed to "sense" emotion from text. The novelty of our approach is twofold: affective information conveyed through text is analyzed (1) by considering the cognitive and appraisal structure of emotions, and (2) by taking into account user preferences.