With the rapid development of the movie industry, it is vital to evaluate and predict a movie’s quality. In this paper, a movie score prediction model is proposed based on the movie plots. Movie data was processed wi...With the rapid development of the movie industry, it is vital to evaluate and predict a movie’s quality. In this paper, a movie score prediction model is proposed based on the movie plots. Movie data was processed with the word2vec method, and the linear regression model and back propagation neural network algorithm were employed to establish the movie score prediction model. The high-quality classic movie plots of high-scoring movies summed up by big data contributed to a high synthesis of the wonderful content of the film. Experimental results show that it is effective in terms of movie evaluation and prediction, and helpful in understanding people’s preferences for movie plots.展开更多
For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In t...For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the image.In addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional emotions.The experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image dataset.This study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.展开更多
Movies and web series are the most popular destinations of crowdfunding,and various approaches have been followed by project initiators.Filmmakers may use specialized or general crowdfunding platforms to raise money,p...Movies and web series are the most popular destinations of crowdfunding,and various approaches have been followed by project initiators.Filmmakers may use specialized or general crowdfunding platforms to raise money,propose fixed or flexible budgets,and use reward-based or equity-based crowdfunding.Crowdfunding may entirely or just partially finance production.To date,no standard model has been used for the production of movies and web series through crowdfunding.Recently,content-based web series on over-the-top(OTT)platforms,such as Netflix and Amazon Prime,have gained popularity.The aim of this study is to fill this conceptual gap and propose a crowdfunding model for movies and web series that can be applied and used to benefit all stakeholders:filmmakers,backers,distributors,platform owners,and the entire future audience.The model consists of nine chronologically interlinked phases and six types of flows:information/content,funds,audition,decision-making,content,promotion,and rewards.The conceptual model proposed herein is based on a critical analysis of the extant literature in the field,mainly qualitative analyses performed on successfully and unsuccessfully crowdfunded and professional films in connection with the current technical platform functionalities.展开更多
基金Natural Science Foundation of Jilin Provincial Science and Technology Department (20180101016JC)Science and Technology Development Plan of Jilin Province (20180101054JC).
文摘With the rapid development of the movie industry, it is vital to evaluate and predict a movie’s quality. In this paper, a movie score prediction model is proposed based on the movie plots. Movie data was processed with the word2vec method, and the linear regression model and back propagation neural network algorithm were employed to establish the movie score prediction model. The high-quality classic movie plots of high-scoring movies summed up by big data contributed to a high synthesis of the wonderful content of the film. Experimental results show that it is effective in terms of movie evaluation and prediction, and helpful in understanding people’s preferences for movie plots.
基金Supported by the Open Project of Key Laboratory of Audio and Video Restoration and Evaluation(2021KFKT005)。
文摘For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the image.In addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional emotions.The experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image dataset.This study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.
基金funded by Woosong University Academic Research in 2021
文摘Movies and web series are the most popular destinations of crowdfunding,and various approaches have been followed by project initiators.Filmmakers may use specialized or general crowdfunding platforms to raise money,propose fixed or flexible budgets,and use reward-based or equity-based crowdfunding.Crowdfunding may entirely or just partially finance production.To date,no standard model has been used for the production of movies and web series through crowdfunding.Recently,content-based web series on over-the-top(OTT)platforms,such as Netflix and Amazon Prime,have gained popularity.The aim of this study is to fill this conceptual gap and propose a crowdfunding model for movies and web series that can be applied and used to benefit all stakeholders:filmmakers,backers,distributors,platform owners,and the entire future audience.The model consists of nine chronologically interlinked phases and six types of flows:information/content,funds,audition,decision-making,content,promotion,and rewards.The conceptual model proposed herein is based on a critical analysis of the extant literature in the field,mainly qualitative analyses performed on successfully and unsuccessfully crowdfunded and professional films in connection with the current technical platform functionalities.
