Movie’s box office performance prediction: An approach based on movie’s script, text mining and deep learning

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceA capacidade de prever a bilheteria de filmes tem sido atividade de grande interesse para investigadores. Entretanto, parcela significativa destes estudos concentra-se no uso de variáveis disponíveis apenas nos estágios de produção e pós-produção de filmes. O objetivo deste trabalho é desenvolver um modelo preditivo de bilheteria baseando-se apenas em informações dos roteiros dos filmes, por meio do uso de técnicas de processamento de linguagem natural (PLN), mineração de texto e de redes neuronais profundas. Essa abordagem visa otimizar a tomada de decisão de investidores em uma fase ainda inicial dos projetos, com foco específico na melhoria dos processos seletivos da Agência Nacional do Cinema do Brasil.The ability to predict movies box-office has been a field of interest for many researchers. However, most of these studies are concentrated on variables that are available only in later stages as in production and pos-production phase of films. The objective of this work is to develop a predictive model to forecast movie box-office performance based only on information in the movie script, using natural language processing techniques, text mining and deep learning neural networks. This approach aims to optimize the investor’s decision-making process at earlier steps of the project, with special focus on the selection process of the Brazilian Film Agency (ANCINE – Agência Nacional do cinema)

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Risky Business: The Impact of Property Rights on Investment and Revenue in the Film Industry

Our paper tests a key prediction of property rights theory: that agents respond to marginal incentives embedded in property rights, when making non-contractible, revenue-enhancing investments. (Grossman and Hart, 1986; Hart and Moore, 1990). Using rich project-level data from the US film industry, we exploit variation in property right allocations, investment choices, and film revenues to test the distinctive aspects of property rights theory. Empirical tests of these key theoretical predictions have been relatively sparse due to the lack of appropriate data. The US film industry displays two distinct allocations of property rights, which differentially affect marginal returns on a particular class of investments. Studio-financed films are produced and distributed by studios which take in the lion’s share of revenue. In contrast, independent films are distributed by studios under revenue sharing agreements, which give studios 30-40% of the revenue stream. Under either regime, the allocation of scarce marketing resources is determined by and paid for by the studio. After accounting for the endogenous nature of property-right allocations, we find that studio-financed films receive superior marketing investments compared to independent films and that these investments fully mediate the positive effect of vertical integration on film revenues. As a result, this study contributes to the empirical literature on property rights by showing that both predicted linkages (from marginal returns to investment and from investment to revenue) exist in a single empirical setting

Movies, TV programs and Youtube channels

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2021.8. 조성준.The content market, including video content market, is a high-risk, high-return industry. Because the cost of copying and distributing the created video content is very low, large profit can be generated upon success. However, as content is an experience good, its quality cannot be judged before purchase. Hence, marketing has an important role in the content market because of the asymmetry of information between suppliers and consumers. Additionally, it has the characteristics of One Source Multi Use; if it is successful, additional profits can be created through various channels. Therefore, it is important for the content industry to correctly distinguish content with a high probability of success from the one without it and to conduct effective marketing activities to familiarize consumers with the product. Herein, we propose a methodology to assist in data-based decision-making using machine learning models and help in identifying problematic issues in video content markets such as movies, TV programs, and over-the-top (OTT) market. In the film market, although marketing is very important, decisions are still made based on the sense of practitioners. We used the market research data collected through online and offline surveys to learn a model that can predict the number of audiences on the opening-week Saturday, and then use the learned model to propose a method for effective marketing activities. In the TV program market, programming is performed to improve the overall viewership by matching TV programs and viewer groups well. We learn a model that predicts the audience rating of a program using the characteristics of the program and the audience-rating information of the programs before, after, and at the same time, and use the resulting data to assist in decision-making to find the optimal programming scenario. The OTT market is facing a new problem of user's perception bias caused by the “recent recommendation” system. In the fields of politics and news particularly, if the user does not have access to different viewpoints because of the recommendation service, it may create and/or deepen a bias toward a specific political view without the user being aware of it. In order to compensate for this, it is important to use the recommended channel while the user is well aware of what kind of channel it is. We built a channel network in the news/political field using the data extracted from the comments left by users on the videos of each channel. In addition, we propose a method to compensate for the bias by classifying networks into conservative and progressive channel clusters and presenting the topography of the political tendencies of YouTube channels.1 Introduction 1 2 Prediction of Movie Audience on First Saturday with Decision Trees 5 2.1 Background 5 2.2 Related work 9 2.3 Predictive model construction 15 2.3.1 Data 15 2.3.2 Target variable 17 2.3.3 Predictor variable 19 2.3.4 Decision Tree and ensemble prediction models 28 2.4 Prediction model evaluation 29 2.5 Summary 37 3 Prediction of TV program ratings with Decision Trees 40 3.1 Background 40 3.2 Related work 42 3.2.1 Research on the ratings themselves 42 3.2.2 Research on broadcasting programming 44 3.3 Predictive model construction 45 3.3.1 Target variable 45 3.3.2 Predictor variable 46 3.3.3 Prediction Model 48 3.4 Prediction model evaluation 50 3.4.1 Data 50 3.4.2 Experimental results 51 3.5 Optimization strategy using the predictive model 54 3.5.1 Broadcasting programming change process 56 3.5.2 Case Study 57 3.6 Summary 60 4 Relation detection of YouTube channels 62 4.1 Background 62 4.2 Related work 65 4.3 Method 67 4.3.1 Channel representation 68 4.3.2 Channel clustering with large k and merging clusters by keywords 71 4.3.3 Relabeling with RWR 73 4.3.4 Isolation score 74 4.4 Result 74 4.4.1 Channel representation 74 4.4.2 Channel clustering with large k and merging clusters by keywords 76 4.4.3 Relabeling with RWR 77 4.4.4 Isolation score 79 4.5 Discussion 80 4.5.1 On the Representativeness of the Channel Preferences of the Users from Their Comments 80 4.5.2 On Relabeling with RWR 82 4.6 Summary 83 5 Conclusion 85 5.1 Contribution 85 5.2 Future Direction 87 Bibliography 91 국문초록 110박

Essays on Empirical Industrial Organization and Networks

This dissertation is composed of two essays in the field of empirical industrial organization. They both examine how network structures arise in and affect markets. I focus on two industries. The first one is the airline industry, and the second one is the motion picture industry. The first essay (chapter) studies airline networks. Airlines often match higher pas- senger density with higher flight frequency. Meanwhile, a higher frequency reduces schedule delays, creating better service quality. This suggests that, on airline networks, the value of a link to passengers increases with the density on that link. I estimate a discrete choice model for U.S. airlines with endogenous link density. The model allows me to account for changes in frequencies in counterfactual experiments. I derive implications for airline pricing, market concentration and hub-and-spoke networks. The second essay studies product entry in the presence of firm learning from the market outcomes of past products. Focusing on the U.S. motion picture industry, I construct a network capturing the similarity amongst the movies released in the last decades. I develop and estimate a model of how the network evolves. Risk averse firms make go/no go decisions on candidate products that arrive over time and can be either novel or similar to various previous products. I demonstrate that learning is an important factor in entry decisions and provide insights on the innovation vs. imitation tradeoff. In particular, I find that one firm benefits substantially from the learning by the other firms. I find that big-budget movies benefit more from imitation, but small-budget movies favor novelty. This leads to interesting market dynamics that cannot be produced by a model without learning

Globally Distributed R&D Work in a Marketing Management Support Systems (MMSS) Environment

Globalisation, liberalization and rapid technological developments have been changing business environments drastically in the recent decades. These trends are increasingly exposing businesses to market competition and thus intensifying competition. In such an environment, the role of marketing management support systems (MMSS) becomes exceedingly important for the long-term growth of an organisations marketing expertise and success. In this paper, we discuss the evolution of a globally distributed R&D project spanning three continents in developing an MMSS for the motion picture industry. We first provide the conceptual background of the MMSS and knowledge management systems relevant for our work. We then provide a detailed case study of our MMSS implementation. We specifically focus on the following elements of our work: globally distributed R&D efforts, knowledge elements, and fit between demand and supply sides of MMSS. We conclude with a discussion of implications for future research in this area