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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Piçarra, N., Reis, E., Chambel, T.  & Arriaga, P. (2022). Searching, navigating, and recommending movies through emotions: A scoping review. Human Behavior and Emerging Technologies. 2022
Exportar Referência (IEEE) 
N. J. Piçarra et al.,  "Searching, navigating, and recommending movies through emotions: A scoping review", in Human Behavior and Emerging Technologies, vol. 2022, 2022
Exportar BibTeX 
@null{piçarra2022_1713526859057,
	year = "2022",
	url = "https://www.hindawi.com/journals/hbet/2022/7831013/"
}
Exportar RIS 
TY  - GEN
TI  - Searching, navigating, and recommending movies through emotions: A scoping review
T2  - Human Behavior and Emerging Technologies
VL  - 2022
AU  - Piçarra, N.
AU  - Reis, E.
AU  - Chambel, T. 
AU  - Arriaga, P.
PY  - 2022
SN  - 2578-1863
DO  - 10.1155/2022/7831013
UR  - https://www.hindawi.com/journals/hbet/2022/7831013/
AB  - Movies offer viewers a broad range of emotional experiences, providing entertainment, and meaning. Following the PRISMA-ScR guidelines, we reviewed the literature on digital systems designed to help users search and browse movie libraries and offer recommendations based on emotional content. Our search yielded 83 eligible documents (published between 2000 and 2021). We identified 22 case studies, 34 empirical studies, 26 proof of concept, and one theoretical paper. User transactions (e.g., ratings, tags) were the preferred source of information. The documents examined approached emotions from both categorical (n=35) and dimensional (n=18) perspectives, and nine documents offer a combination of both approaches. Although there are several authors mentioned, the references used are frequently dated, and 12 documents do not mention the author or the model used. We identified 61 words related to emotion or affect. Documents presented on average 1.36 positive terms and 2.64 negative terms. Sentiment analysis () is frequently used for emotion identification, followed by subjective evaluations (n= 15), movie low-level audio and visual features (n = 11), and face recognition technologies (n = 8). We discuss limitations and offer a brief review of current emotion models and research.
ER  -