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