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A Model of Textual Affect Sensing Using Real-World Knowledge (2003)

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by Hugo Liu , Henry Lieberman , Ted Selker
Citations:195 - 12 self
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BibTeX

@INPROCEEDINGS{Liu03amodel,
    author = {Hugo Liu and Henry Lieberman and Ted Selker},
    title = {A Model of Textual Affect Sensing Using Real-World Knowledge},
    booktitle = {},
    year = {2003},
    pages = {125--132}
}

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Abstract

This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.

Keyphrases

textual affect    real-world knowledge    lexical affinity    affective quality    new robustness implication    hand-crafted model    basic emotion category    new approach    statistical method    keyword spotting    previous approach    car accident    novel way    large-scale real-world knowledge    commonsense approach    inherent affective nature    everyday situation    natural language   

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