We then empirically measure the response of audiences to different degrees of innovation in successive musical album releases, by using a multi-attribute musical description of songs, together with their corresponding radio plays and critics' reviews. We develop a theory rooted in classical behavioral economics of reference-building, and consider preference structures of habit formation and satiation. In this paper, we provide a general framework that incorporates the dynamics of these references towards addressing the classical dilemma of incremental versus radical innovation. Through a repeated interaction with the artist's music, the audiences build their own expectations about the future releases which affect the overall market reception. Newly released music by a certain artist is never assessed in isolation by the audiences, who tend to compare it with the previous musical catalogue of the corresponding artist. We integrate our method into larger theoretical discussions on audiences’ perception of racial minorities and illuminate future research trajectories towards the computational assessment of racial biases in audiovisual narratives.
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We apply our approach on a set of 89 popular, full-length movies, demonstrating that this method provides a scalable examination of racial inclusion in film production and predicts movie performance. In this paper, we combine methodological tools from computer vision and network science to develop a content analytic framework for identifying visual and structural racial biases in film productions. Currently, there are no clearly defined, standardized, and scalable metrics for taking stock of racial minorities’ cinematographic representation. Characters from racially underrepresented groups receive less screen time, fewer central story positions, and frequently inherit plotlines, motivations, and actions that are primarily driven by White characters. In the Hollywood film industry, racial minorities remain underrepresented.
This study also conducts experiments that highlight the importance of story popularity on release time related to movie success. The proposed features enhanced the accuracy by 11.9% and achieves an F1 Score of 75.1% in comparison to the state-of-the-art. A hybrid voting based classifier is created using Gradient Boosting, Random Forest, Support Vector Machine, Multilayer Perceptron, and Deep Learning classifiers to forecast the success of the movies in the development phase.
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Multiple time series are generated representing the sentiment of a story and plot topics that are collectively termed as “say” Story popularity.
In this research, novel time series based features are proposed for “say” Story popularity in order to predict the movie success accurately. A movie plot is established during the development phase and it is crucial aspect for determining the movie success. Movie success prediction in the development phase is considered a challenging task due to the availability of very limited information.