Programming and Deploying Java Mobile Agents Aglets
Programming and Deploying Java Mobile Agents Aglets
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
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Emotional e-mail classification is one of the important issues in the service oriented organizations. E-mails are served in a first come first serve basis. Few e-mails express the unfair treatment or dissatisfaction of service. It is essential to serve such e-mails with a high priority. In this paper an attempt is made to identify such mails which express the strong emotions of the customers / stakeholders. This system classifies the e-mails in to three categories via positive, negative and other mails. An adaptive machine learning algorithm that uses combined SVD and KNN methods is developed to solve the problem of emotional e-mail classification. Also an emotional dictionary is used as a central component of this system that serves various emotional words and phrases for classification. The system also adaptive in nature and adapts various new words and phrases that explicates the emotion.