Abstract
One of the biggest hurdles for the utilization of machine learning in interdisciplinary projects is the need for annotated training data which is costly to create. Emotion annotation is a notoriously difficult task, and the current annotation schemes which are based on psychological theories of human interaction are not always the most conducive for the creation of reliable emotion annotations, nor are they optimal for annotating emotions in the modality of text. This paper discusses the theory, history, and challenges of emotion annotation, and proposes improvements for emotion annotation tasks based on both theory and case studies. These improvements focus on rethinking the categorization of emotions and the overlap and disjointedness of emotion categories.
| Original language | English |
|---|---|
| Pages (from-to) | 134-144 |
| Number of pages | 11 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2865 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 5th Conference Digital Humanities in the Nordic Countries, DHN 2020 - Riga, Latvia Duration: 2020 Oct 21 → 2020 Oct 23 |
Keywords
- Emotion annotation
- Textual expressions of emotions
- Theories of emotion
ASJC Scopus subject areas
- Computer Science(all)
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In: CEUR Workshop Proceedings, Vol. 2865, 2020, p. 134-144.
Research output: Contribution to journal › Conference article › peer-review
}
TY - JOUR
T1 - Emotion annotation
T2 - 5th Conference Digital Humanities in the Nordic Countries, DHN 2020
AU - Öhman, Emily
N1 - Funding Information: The study has been supported by the Acupuncture Trialists’ Collaboration, which includes physicians, clinical trialists, biostatisticians, practicing acupuncturists, and others. The collaborators within the Acupuncture Trialists’ Collaboration are Mac Beckner, MIS, Information Technology and Data Management Center, Samueli Institute, Alexandria, Virginia; Brian Berman, MD, University of Maryland School of Medicine and Center for Integrative Medicine, College Park, Maryland; Benno Brinkhaus, MD, Institute for Social Medicine, Epidemiology and Health Economics, Charité University Medical Center, Berlin, Germany; Remy Coeytaux, MD, PhD, Department of Community and Family Medicine, Duke University, Durham, North Carolina; Angel M. Cronin, MS, Dana-Farber Cancer Institute, Boston, Massachusetts; Hans-Christoph Diener, MD, PhD, Department of Neurology, University of Duisburg-Essen, Germany; Heinz G. Endres, MD, Ruhr–University Bochum, Bochum, Germany; N. E. Foster, DPhil, BSc(Hons), Arthritis Research UK Primary Care Centre, Research Institute of Primary Care and Health Sciences, Keele University, Newcastle-under-Lyme, Staffordshire, England; Juan Antonio Guerra de Hoyos, MD, Andalusian Integral Plan for Pain Management, and Andalusian Health Service Project for Improving Primary Care Research, Sevilla, Spain; Michael Haake, MD, PhD, Department of Orthopedics and Traumatology, SLK Hospitals, Heilbronn, Germany; Dominik Irnich, MD, Interdisciplinary Pain Centre, University of Munich, Munich, Germany; Wayne B. Jonas, MD, Samueli Institute, Alexandria, Virginia; Kai Kronfeld, PhD, Interdisciplinary Centre for Clinical Trials (IZKS Mainz), University Medical Centre Mainz, Mainz, Germany; Lixing Lao, PhD, University of Maryland and Center for Integrative Medicine, College Park, Maryland; G. Lewith, MD, FRCP, Complementary and Integrated Medicine Research Unit, Southampton Medical School, Southampton, England; K. Linde, MD, Institute of General Practice, Technische Universität München, Munich, Germany; H. MacPherson, PhD, Complementary Medicine Evaluation Group, University of York, York, England; Eric Manheimer, MS, Center for Integrative Medicine, University of Maryland School of Medicine, College Park, Maryland; Alexandra Maschino, MPH, Department of International Health, Johns Hopkins University, Baltimore, Maryland; Dieter Melchart, MD, PhD, Centre for Complementary Medicine Research (Znf), Technische Universität München, Munich, Germany; Albrecht Molsberger, MD, PhD, German Acupuncture Research Group, Duesseldorf, Germany; K. J. Sherman, PhD, MPH, Group Health Research Institute, Seattle, Washington; Hans Trampisch, PhD, Department of Medical Statistics and Epidemiology, Ruhr–University Bochum, Germany; Jorge Vas, MD, PhD, Pain Treatment Unit, Dos Hermanas Primary Care Health Center (Andalusia Public Health System), Dos Hermanas, Spain; A. J. Vickers (collaboration chair), DPhil, Memorial Sloan-Kettering Cancer Center, New York, New York; Peter White, PhD, School of Health Sciences, University of Southampton, England; Lyn Williamson, MD, MA (Oxon), MRCGP, FRCP, Great Western Hospital, Swindon, and Oxford University, Oxford, England; Stefan N. Willich, MD, MPH, MBA, Institute for Social Medicine, Epidemiology, and Health Economics, Charité University Medical Center, Berlin, Germany; Funding Information: The Acupuncture Trialists’ Collaboration is funded by an R21 (AT004189I and an R01 [AT006794] from the National Center for Complementary and Alternative Medicine [NCCAM] at the National Institutes of Health [NIH] to A.J.V.) and by a grant from the Samueli Institute. H. MacPherson’s work on this project was funded in part by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10186). G. Lewith’s contribution has been supported in part by the School for Primary Care Research, which is part of the NIHR. C. M. Witt’s work has been supported by the Carstens Foundation within the grant for the Chair for Complementary Medicine Research. N. E. Foster has been supported by an NIHR Research Professorship (NIHR-RP-011-015). The views expressed in this publication are those of the author(s) and not necessarily those of the NCCAM, NHS, NIHR, or the Department of Health in England. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding Information: The authors have no conflicts of interest to declare. The Acupuncture Trialists' Collaboration is funded by an R21 (AT004189I and an R01 [AT006794] from the National Center for Complementary and Alternative Medicine [NCCAM] at the National Institutes of Health [NIH] to A.J.V.) and by a grant from the Samueli Institute. H. MacPherson's work on this project was funded in part by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10186). G. Lewith's contribution has been supported in part by the School for Primary Care Research, which is part of the NIHR. C. M. Witt's work has been supported by the Carstens Foundation within the grant for the Chair for Complementary Medicine Research. N. E. Foster has been supported by an NIHR Research Professorship (NIHR-RP-011-015). The views expressed in this publication are those of the author(s) and not necessarily those of the NCCAM, NHS, NIHR, or the Department of Health in England. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2020
Y1 - 2020
N2 - One of the biggest hurdles for the utilization of machine learning in interdisciplinary projects is the need for annotated training data which is costly to create. Emotion annotation is a notoriously difficult task, and the current annotation schemes which are based on psychological theories of human interaction are not always the most conducive for the creation of reliable emotion annotations, nor are they optimal for annotating emotions in the modality of text. This paper discusses the theory, history, and challenges of emotion annotation, and proposes improvements for emotion annotation tasks based on both theory and case studies. These improvements focus on rethinking the categorization of emotions and the overlap and disjointedness of emotion categories.
AB - One of the biggest hurdles for the utilization of machine learning in interdisciplinary projects is the need for annotated training data which is costly to create. Emotion annotation is a notoriously difficult task, and the current annotation schemes which are based on psychological theories of human interaction are not always the most conducive for the creation of reliable emotion annotations, nor are they optimal for annotating emotions in the modality of text. This paper discusses the theory, history, and challenges of emotion annotation, and proposes improvements for emotion annotation tasks based on both theory and case studies. These improvements focus on rethinking the categorization of emotions and the overlap and disjointedness of emotion categories.
KW - Emotion annotation
KW - Textual expressions of emotions
KW - Theories of emotion
UR - http://www.scopus.com/inward/record.url?scp=85106050954&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106050954&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85106050954
SN - 1613-0073
VL - 2865
SP - 134
EP - 144
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 21 October 2020 through 23 October 2020
ER -