Abstract
Social robots and agents can interact with people better if they can infer their affective state (emotions). While they cannot yet recognise affective state from tone and body language, they can use the fragments of speech that they (over)hear. We show that emotions – as conventionally framed – are difficult to detect. We suggest, from empirical results, that this is because emotions are the wrong granularity; and that emotions contain subemotions that are much more clearly separated from one another, and so are both easier to detect and to exploit.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Trust is included in many lists of basic emotions, including the NRC lexicon. However, this does not seem natural: ‘I feel trust’ seems to be more of an attitudinal statement than an affective one, especially as it must have an object. Compare this to ‘I feel angry’.
- 2.
Each document-word matrix is different, but, for anger, the amount of variation captured by truncating at \(k=3\) is 55%.
References
Costa Jr., P.T., McCrae, R.R.: Domains and facets: hierarchical personality assessment using the revised neo personality inventory. J. Pers. Assess. 64(1), 21–50 (1995)
Davidson, R., Sherer, K., Goldsmith, H. (eds.): Handbook of Affective Sciences. Oxford University Press, Oxford (2009)
Ekman, P.: Basic emotions. In: Dalgleish, T., Power, M. (eds.) Handbook of Cognition and Emotion, pp. 45–60. John Wiley and Sons (1999)
Fillenbaum, S., Rapoport, A.: Structures in the Subjective Lexicon. Academic Press, New York (1971)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)
Plutchik, R.: The nature of emotions. Am. Sci. 89(4), 344–350 (2001)
Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Skillicorn, D., Alsadhan, N., Billingsley, R., Williams, MA. (2019). Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-18305-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18304-2
Online ISBN: 978-3-030-18305-9
eBook Packages: Computer ScienceComputer Science (R0)