TY - JOUR
T1 - Searching, navigating, and recommending movies through emotions
T2 - a scoping review
AU - Piçarra, Nuno
AU - Reis, Eduardo
AU - Chambel, Teresa
AU - Arriaga, Patrícia
N1 - Publisher Copyright:
© 2022 Nuno Piçarra et al.
PY - 2022
Y1 - 2022
N2 - 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 a 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 author or 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 (n=31) 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.
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 a 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 author or 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 (n=31) 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.
UR - http://www.scopus.com/inward/record.url?scp=85143976931&partnerID=8YFLogxK
U2 - 10.1155/2022/7831013
DO - 10.1155/2022/7831013
M3 - Review article
SN - 2578-1863
VL - 2022
JO - Human Behavior and Emerging Technologies
JF - Human Behavior and Emerging Technologies
M1 - 7831013
ER -