The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Modelling Valence and Arousal in Facebook posts
Open accessAbstract
Daniel Preoţiuc-Pietro, H. Andrew Schwartz, Gregory Park, Johannes Eichstaedt, Margaret Kern, Lyle Ungar, Elisabeth Shulman. Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2016.
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Cites
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · cited 2×
“In this study, we focus on the most popular dimensional model of emotion: the circumplex model introduced in (Russell, 1980).”
BackgroundMethodAn argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
POSNER, J., RUSSELL, J. A., & PETERSON, B. S. (2005) · Development and Psychopathology · doi:10.1017/s0954579405050340 · Cited by 2,602 · cited 2×
“For example, fear is a state involving the combination of negative valence and high arousal (Posner et al., 2005).”
BackgroundNorms of valence, arousal, and dominance for 13,915 English lemmas
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013) · Behavior Research Methods · doi:10.3758/s13428-012-0314-x · Cited by 1,959
Aging and motivated cognition: the positivity effect in attention and memory
Mather, M., & Carstensen, L. L. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.08.005 · Cited by 1,910 · cited 2×
“With regards to age, an age positivity bias has been found, where positive emotion expression increases with age (Mather and Carstensen, 2005; Kern et al., 2014).”
BackgroundAffect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · cited 1×
“…in Natural Language Processing (NLP) has been focused mostly on Ekman’s model of emotion (Ekman, 1992) which posits the existence of six basic emotions: anger, disgust, fear, joy, sadness and surprise (Strappar-ava and Valitutti, 2004; Strapparava and Mihalcea, 2008; Calvo and D’Mello, 2010).”
BackgroundSentiment strength detection in short informal text
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449
Sentiment strength detection for the social web
Thelwall, M., Buckley, K., & Paltoglou, G. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21662 · Cited by 1,062
reconstructing the past: a century of ideas about emotion in psychology
Gendron, M., & Feldman Barrett, L. (2009) · Emotion Review · doi:10.1177/1754073909338877 · Cited by 361 · cited 1×
“Emotion classification is a widely debated topic in psychology (Gendron and Barrett, 2009).”
BackgroundDemographic Factors Improve Classification Performance
Hovy, D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1073 · Cited by 148
Cited by
Text‐based emotion detection: Advances, challenges, and opportunities
Acheampong, F. A., Wenyu, C., & Nunoo‐Mensah, H. (2020) · Engineering Reports · doi:10.1002/eng2.12189 · Cited by 367
DLATK: Differential Language Analysis ToolKit
Schwartz, H. A., Giorgi, S., Sap, M., Crutchley, P., Ungar, L., & Eichstaedt, J. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/d17-2010 · Cited by 130
Self-citationSuicide Risk Assessment with Multi-level Dual-Context Language and
Matero, M., Idnani, A., Son, Y., Giorgi, S., Vu, H., Zamani, M., Limbachiya, P., Guntuku, S. C., & Schwartz, H. A. (2019) · Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology · doi:10.18653/v1/w19-3005 · Cited by 102
Self-citationLanguage of ADHD in Adults on Social Media
Guntuku, S. C., Ramsay, J. R., Merchant, R. M., & Ungar, L. H. (2019) · Journal of Attention Disorders · doi:10.1177/1087054717738083 · Cited by 89
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Metadata
- Authors
- Preoţiuc-Pietro, D., Schwartz, H. A., Park, G., Eichstaedt, J., Kern, M., Ungar, L., & Shulman, E.
- Venue
- Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Published
- 2016
- Pages
- 9-15
- OpenAlex
- W2339570520
- Cited by
- 165
- References
- 10
- FWCI
- 8.35
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Preoţiuc-Pietro, D., Schwartz, H. A., Park, G., Eichstaedt, J., Kern, M., Ungar, L., & Shulman, E. (2016). Modelling Valence and Arousal in Facebook posts. Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. https://doi.org/10.18653/v1/w16-0404
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