The power of twitter on predicting box office revenues

Over the last few years there has been an extraordinary surge of social networking and microblogging services. Twitter is a social network that focuses on social and news media. The Twitter data stream allows access to tweets, timestamps and locations of users. This enables us to capture the trends and patterns of rapidly evolving worldwide events. We use the Twitter data stream for the prediction of consumer preferences in the movie industry and estimate how successful the movie will be in the first and second weekends since its release date. The study provides evidence to suggest that frequencies of contemporaneous tweets and a consensus measure of public sentiment are useful for predicting box-office revenues, implying that any publicity is good publicity in word-of-mouth (WOM) and online viral marketing. Sentiment analysis based on tweets suggests that more extreme sentiment has more impact, and that the more negative the tweets about a movie are, the higher its revenue will be, in contrast with the classic theory of diffusion in news media

Prediction of Broadcast Volume and Analysis of Influencing Factors About Online Videos

With the development of the Internet and the continuous expansion of the online video market scale, it is increasingly important to accurately predict broadcast volume before the launch of videos. On the one hand, it can provide investors and producers with recommendation scheme for video shooting. On the other hand, it can fully understand users\u27 preferences and find videos which potentially to be popular. In allusion to problems of low predictive accuracy and lack of practical application value in video prediction research, this paper, based on the related data of online video website TED, obtains a prediction model with high-precision broadcast volume through feature selection and model fusion. Further, it analyzes the impact of video themes, the number of languages and official events, which can provide some reference for investors and producers of different scales before the videos go online

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

Predicting Movie Success with Machine Learning Techniques: Theoretical and Methodological Approaches to Improve Model Performance

학위논문 (석사)-- 서울대학교 대학원 : 경영학과 경영정보 전공, 2016. 2. 박진수.Previous studies on predicting the box-office performance of a movie using machine learning techniques have shown practical levels of predictive accuracy. However, their efforts to improve the model accuracy have been limited only to the methodological perspective. In this paper, we combine a theory-driven approach and a methodology-driven approach to further increase the accuracy of prediction models. First, we add a new feature derived from the theory of transmedia storytelling. Such theory-driven feature selection not only increases the forecast accuracy, but also enhances the explanatory power of the prediction model. Second, we use an ensemble approach, which has rarely been adopted in the research on predicting box-office performance. As a result, our model, Cinema Ensemble Model (CEM), outperforms the prediction models from the past studies using machine learning algorithms. We suggest that CEM can be extensively used for industrial experts as a powerful tool for improving decision-making process.1. Introduction 4 2. Related Works 5 2.1. Predictive studies in the movie domain 5 2.2. The theory of transmedia storytelling 6 3. Methodology 7 3.1. Building an Ensemble Model for Predicting Movie Success 7 3.2. Descriptions of Learning Algorithms for Component Models 8 3.2.1. Adaptive Tree Boosting 8 3.2.2. Gradient Tree Boosting 9 3.2.3. Linear Discriminant 9 3.2.4. Logistic Regression 10 3.2.5. Random Forests 10 3.2.6. Support Vector Classifier 10 3.3. Discretization of the Movie Success 11 3.4. Feature Definition 11 3.4.1. Genre 12 3.4.2. Sequel 12 3.4.3. Number of Plays at the Initial Day of Release 13 3.4.4. Movie Buzz before the Release 13 3.4.5. Transmedia Storytelling 14 3.4.6. Star Buzz (i.e., Star Power) 15 4. Data Collection 16 5. Analysis 17 5.1. Performance Metrics 17 5.2. Candidate-model Performance 18 5.3. Cinema Ensemble Model (CEM) Performance 19 5.4. Performance Improvement by Transmedia Storytelling Feature 21 6. Discussion 22 References 25Maste

Predicting movie success with machine learning techniques: ways to improve accuracy

This study was supported by research grants from the Institute of Management Research at the Seoul National University

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

Automatic Pavement Crack Recognition Based on BP Neural Network

A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application

A study of social media usage and interactions

In-depth interviews were conducted where 30 participants provided information about their social media usage and interactions with films online. Participants answered questions dealing with their average social media usage, their favorite social media sites, their history with film interaction online, their knowledge of the film Suicide Squad, their current interactions with the film online, and how they viewed Suicide Squad's social media campaigns and how they believed they could be improved. Individuals with high usage of Facebook and Instagram showed high levels of interactions with the film's accounts. Participants who used Facebook, Twitter, and Instagram daily were aware of the social media accounts for the film and believed the Facebook and Instagram accounts were the strongest of the three. Whiting and Williams theory of uses and gratifications for social media (2013) was used as a basis to discover why participants use social media to better explain why they specifically use social media for film interaction. This theory showed five major categories participants belonged to for why they interact with films online

Using twitter as a source of information for time series prediction

This project aims to assess whether tapping into the wealth of information that Twitter has to offer can positively affect the prediction of time series. We intend to accomplish our goal by means of applying multiple machine learning predictive models and text mining techniques, all organized within a framework that should be general enough to allow the realization of any similar task

Box Office Forecasting considering Competitive Environment and Word-of-Mouth in Social Networks: A Case Study of Korean Film Market

Accurate box office forecasting models are developed by considering competition and word-of-mouth (WOM) effects in addition to screening-related information. Nationality, genre, ratings, and distributors of motion pictures running concurrently with the target motion picture are used to describe the competition, whereas the numbers of informative, positive, and negative mentions posted on social network services (SNS) are used to gauge the atmosphere spread by WOM. Among these candidate variables, only significant variables are selected by genetic algorithm (GA), based on which machine learning algorithms are trained to build forecasting models. The forecasts are combined to improve forecasting performance. Experimental results on the Korean film market show that the forecasting accuracy in early screening periods can be significantly improved by considering competition. In addition, WOM has a stronger influence on total box office forecasting. Considering both competition and WOM improves forecasting performance to a larger extent than when only one of them is considered