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.
For more information email:
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