Project Team:
Hugo Liu, Henry Lieberman, Ted Selker


Screen Shots:
Empathy Buddy
Textual Affect Sensing of Little Red Riding Hood
Textual Affect Sensing of The Fall of the House of Usher


A Model of Textual Affect Sensing using Real-World Knowledge (PDF)

Automatic Affective Feedback in an Email Browser (PDF)
Psychologist William James noted that the recognition of emotion is intimately related to traditions and culture. Based on the thesis that much of our emotional attitudes are dictated by our culture's "common sense" about everyday situations, this project uses large-scale affective commonsense from Open Mind (thousands of facts relating everyday situations to emotions) to analyze the broad emotional qualities of sentences. User evaluations from an experimental application that gives email users automatic affective feedback via Chernov faces show that our text analysis method is effective. Our approach addresses many of the limitations of the existing approaches to textual affect classification (keyword spotting, lexical affinity, hand-crafted models, statistical NLP) by offering greater robustness, and extensibility. With our approach, text can be classified on the individual sentence-level. We believe that this allows for a higher degree of interactivity in affective applications.

Currently we are exploring an affective text-to-speech application using the Emotus Ponens text analyzer engine, and planning a release of the EP API.

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