Pregled bibliografske jedinice broj: 1208619
Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning
Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning // Applied Sciences-Basel, 12 (2022), 15; 7864, 18 doi:10.3390/app12157864 (međunarodna recenzija, članak, znanstveni)
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Naslov
Investigation of Relationships between Discrete
and Dimensional Emotion Models in Affective
Picture Databases Using Unsupervised Machine
Learning
Autori
Horvat, Marko ; Jović, Alan ; Burnik, Kristijan
Izvornik
Applied Sciences-Basel (2076-3417) 12
(2022), 15;
7864, 18
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
picture stimuli ; affective pictures databases ; statistical analysis ; clustering ; emotion ; affective computing
Sažetak
Digital documents created to evoke emotional responses are intentionally stored in special affective multimedia databases, along with metadata describing their semantics and emotional content. These databases are routinely used in multidisciplinary research on emotion, attention, and related phenomena. Affective dimensions and emotion norms are the most common emotion data models in the field of affective computing, but they are considered separable and not interchangeable. The goal of this study was to determine whether it is possible to statistically infer values of emotionally annotated pictures using the discrete emotion model when the values of the dimensional model are available and vice versa. A positive answer would greatly facilitate stimuli retrieval from affective multimedia databases and the integration of heterogeneous and differently structured affective data sources. In the experiment, we built a statistical model to describe dependencies between discrete and dimensional ratings using the affective picture databases NAPS and NAPS BE with standardized annotations for 1356 and 510 pictures, respectively. Our results show the following: (1) there is a statistically significant correlation between certain pairs of discrete and dimensional emotions in picture stimuli, and (2) robust transformation of picture ratings from the discrete emotion space to well-defined clusters in the dimensional space is possible for some discrete-dimensional emotion pairs. Based on our findings, we conclude that a feasible recommender system for affective dataset retrieval can be developed. The software tool developed for the experiment and the results are freely available for scientific and non-commercial purposes.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
Uključenost u ostale bibliografske baze podataka::
- AGRIS International
- INSPEC
- CAPlus / SciFinder
- CNKI
- DOAJ
- EBSCO
- FRIDOC
- Gale
- INSPIRE
- J-Gate
- OpenAIRE
- OSTI.GOV (U.S. Department of Energy)
- PATENTSCOPE
- ProQuest
- SafetyLit