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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.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Dyer, M.G. Emotions and Their Computations: Three Computer Models. Cognition and Emotion, Vol. 1, No. 3, 323--347, 1987.
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3
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Ekman, P. Facial expression of emotion. American Psychologist, 48, 384--392. 1993.
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4
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5
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Goertzel, B., Silverman, K., Hartley, C., Bugaj, S., and Ross, M. The Baby Webmind Project. Proceedings of AISB 2000.
|
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6
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James, W. What is an emotion? In C. Calhoun & R. C. Solomon (Eds.), What is an emotion? Classic readings in philosophical psychology (pp. 127-141). New York: Oxford University Press. 1984.
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7
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8
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Liu, H., Lieberman, H., and Selker, T. Automatic Affective Feedback in an Email Browser. MIT Media Lab Software Agents Group Technical Report. November, 2002. At: http://web.media.mit.edu/~hugo/.
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9
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Minsky, M. The Emotion Machine. Pantheon, not yet available in hardcopy. Drafts available: http://web.media.mit.edu/~minsky/.
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10
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Mueller, E.T. ThoughtTreasure: A natural language/commonsense platform (Online). http://www.signiform.com/tt/htm/overview.htm. 1998.
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11
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Clifford Nass , Jonathan Steuer , Ellen R. Tauber, Computers are social actors, Proceedings of the SIGCHI conference on Human factors in computing systems: celebrating interdependence, p.72-78, April 24-28, 1994, Boston, Massachusetts, United States
[doi> 10.1145/191666.191703]
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12
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Ortony A., Clore, G. L., and Collins, A. The cognitive structure of emotions. New York: Cambridge University Press. 1988
|
| |
13
|
|
| |
14
|
Singh, P., The public acquisition of commonsense knowledge. Proceedings of AAAI Spring Symposium. Palo Alto, CA, AAAI. 2002.
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CITED BY 37
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Alastair J. Gill , Robert M. French , Darren Gergle , Jon Oberlander, The language of emotion in short blog texts, Proceedings of the ACM 2008 conference on Computer supported cooperative work, November 08-12, 2008, San Diego, CA, USA
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Sara H. Owsley , Kristian J. Hammond , David A. Shamma , Sanjay Sood, Buzz: telling compelling stories, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA
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Cecilia Ovesdotter Alm , Dan Roth , Richard Sproat, Emotions from text: machine learning for text-based emotion prediction, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p.579-586, October 06-08, 2005, Vancouver, British Columbia, Canada
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Ling Chen , Gen-Cai Chen , Cheng-Zhe Xu , Jack March , Steve Benford, EmoPlayer: A media player for video clips with affective annotations, Interacting with Computers, v.20 n.1, p.17-28, January, 2008
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Robert Speer , Jayant Krishnamurthy , Catherine Havasi , Dustin Smith , Henry Lieberman , Kenneth Arnold, An interface for targeted collection of common sense knowledge using a mixture model, Proceedings of the 13th international conference on Intelligent user interfaces, February 08-11, 2009, Sanibel Island, Florida, USA
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INDEX TERMS
Primary Classification:
H.
Information Systems
H.5
INFORMATION INTERFACES AND PRESENTATION (I.7)
H.5.2
User Interfaces (D.2.2, H.1.2, I.3.6)
Subjects:
Interaction styles (e.g., commands, menus, forms, direct manipulation)
Additional Classification:
H.
Information Systems
H.5
INFORMATION INTERFACES AND PRESENTATION (I.7)
H.5.2
User Interfaces (D.2.2, H.1.2, I.3.6)
Subjects:
Graphical user interfaces (GUI);
Theory and methods;
Natural language
I.
Computing Methodologies
I.2
ARTIFICIAL INTELLIGENCE
I.2.7
Natural Language Processing
Subjects:
Text analysis;
Language parsing and understanding;
Language models
General Terms:
Algorithms,
Design,
Human Factors,
Languages,
Theory
Keywords:
affective UI,
affective computing,
commonsense reasoning,
emotions,
open mind commonsense,
story understanding
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