Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction to Emotions and Personality in Personalized Systems Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

9. Affective and Personality Corpora

verfasst von : Ante Odić, Andrej Košir, Marko Tkalčič

Erschienen in: Emotions and Personality in Personalized Services

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this chapter we describe publicly available datasets with personality and affective parameters relevant to the research questions covered by this book. We briefly describe the available data, acquisition procedure, and other relevant details of these datasets. There are three datasets acquired through the users’ natural interaction with different services: LDOS CoMoDa, LJ2M and myPersonality. Two datasets were acquired in controlled, laboratory settings: LDOS PerAff-1 and DEAP. Finally, we also mention four stimuli datasets from the Media Core project: ANET, IADS, ANEW, IAPS, as well as the 1000 songs dataset. We summarise this information for a quick reference to researchers interested in using these datasets or preparing the acquisition procedure of their own.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Mobile-Based Experience Sampling for Behaviour Research
Nächstes Kapitel Modeling User’s Social Attitude in a Conversational System
Literatur
1.
Saucier, G., Goldberg, L.R.: What is beyond the big five? J. Pers. 66, 495–524 (1998)CrossRef
2.
Odić, A., Tkalčič, M., Tasič, J.F., Košir, A.: Predicting and detecting the relevant contextual information in a movie-recommender system. Interact. Comput. 25(1), 74–90 (2013)
3.
Tkalčič, M., Košir, A., Tasič, J.: The LDOS-PerAff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata. J. Multimodal User Interface 7(1–2), 143–155 (2013)
4.
Liu, J.Y., Liu, S.Y., Yang, Y.H.: LJ2M dataset: toward better understanding of music listening behavior and user mood. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2014)
5.
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Patras, I.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)CrossRef
6.
Kosinski, M., Stillwell D.J., Graepel T: Private traits and attributes are predictable from digital records of human behavior. In: Proceedings of the National Academy of Sciences (PNAS) (2013)
7.
Soleymani, M., Caro, M.N., Schmidt, E.M., Sha, C.Y., Yang, Y.H.: 1000 songs for emotional analysis of music. In: Proceedings of the 2nd ACM International Workshop on Crowdsourcing for Multimedia, pp. 1–6 (2013)
8.
Bradley, M.M., Lang, P.J.: Affective Norms for English Text (ANET): affective ratings of text and instruction manual. (Tech. Rep. No. D-1). University of Florida, Gainesville, FL (2007)
9.
Bradley, M.M., Lang, P.J.: International affective digitized sounds (IADS): Stimuli, instruction manual and affective ratings (Tech. Rep. No. B-2). The Center for Research in Psychophysiology, University of Florida, Gainesville, FL (1999)
10.
Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings. Technical report C-1. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL (1999)
11.
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical Report A-8. University of Florida, Gainesville, FL (2008)
12.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)CrossRef
13.
Goldberg, L.R., Johnson, J.A., Eber, H.W., Hogan, R., Ashton, M.C., Cloninger, C.R., Gough, H.G.: The international personality item pool and the future of public-domain personality measures. J. Res. Pers. 84–96 (2006)
14.
Zheng, Y., Mobasher, B., Burke, R.D.: The role of emotions in context-aware Recommendation. In: Decisions@ RecSys, pp 21–28 (2013)
15.
Codina, V., Ricci, F., Ceccaroni, L.: Local context modeling with semantic pre-filtering. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 363–366 (2013)
16.
Košir, A., Odić, A., Kunaver, M., Tkalčič, M., Tasič, J.F.: Database for contextual personalization. Elektrotehniki vestnik 78(5), 270–274 (2011)
17.
Posner, J., Russell, J.A., Peterson, B.S.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(03), 715–734 (2005)CrossRef
18.
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8 (2008)
19.
Tkali, M., Burnik, U., Koir, A.: Using affective parameters in a content-based recommender system for images. User Model. User-Adap. Inter. 20(4), 279–311 (2010)CrossRef
20.
Tkalcic, M., Kunaver, M., Koir, A., Tasic, J.: Addressing the new user problem with a personality based user similarity measure. In First International Workshop on Decision Making and Recommendation Acceptance Issues in Recommender Systems (DEMRA 2011) (2011)
21.
Leshed, G., Kaye, J.J.: Understanding how bloggers feel: recognizing affect in blog posts. In: CHI’06 extended abstracts on Human factors in computing systems, pp. 1019–1024 (2006)
22.
Bertin-Mahieux, T., Ellis, D.P., Whitman, B., Lamere, P.: The million song dataset. In: ISMIR: Proceedings of the 12th International Society for Music Information Retrieval Conference, Miami. Florida, vol. 591–596 (2011)
23.
24.
Kandemir, M., Vetek, A., Gnen, M., Klami, A., Kaski, S.: Multi-task and multi-view learning of user state. Neurocomputing 139, 97–106 (2014)CrossRef
25.
Jirayucharoensak, S., Pan-Ngum, S., Israsena, P.: EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci. World J. (2014)
26.
Park, G., Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Kosinski, M., Stillwell, D.J., Seligman, M.E.: Automatic Personality Assessment Through Social Media Language (2014)
27.
Cantador, I., Fernndez-Tobas, I., Bellogn, A., Kosinski, M., Stillwell, D.: Relating personality types with user preferences in multiple entertainment domains. In: UMAP Workshops (2013)
28.
29.
Soleymani, M., Aljanaki, A., Yang, Y. H., Caro, M. N., Eyben, F., Markov, K., Wiering, F.: Emotional analysis of music: a comparison of methods. In: Proceedings of the ACM International Conference on Multimedia, pp. 1161–1164 (2014)
30.
Weninger, F., Eyben, F., Schuller, B.: On-line continuous-time music mood regression with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5412–5416 (2014)
31.
Metadaten
Titel
Affective and Personality Corpora
verfasst von
Ante Odić
Andrej Košir
Marko Tkalčič
Copyright-Jahr
2016
Buch
Emotions and Personality in Personalized Services

Print ISBN: 978-3-319-31411-2

Electronic ISBN: 978-3-319-31413-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31413-6_9

Neuer Inhalt

    Bildnachweise