Proceedings ArticleDOI
Role of different factors in predicting movie success
Anand Bhave,Himanshu Kulkarni,Vinay Biramane,Pranali Kosamkar +3 more
- pp 1-4
TLDR
This paper suggests that the integration of both the classical and the social factors (anticipation and user feedback) and the study of interrelation among the classical factors will lead to more accuracy.Abstract:
Due to rapid digitization and emergence of social media the movie industry is growing by leaps and bounds. The average number of movies produced per year is greater than 1000. So to make the movie profitable, it becomes a matter of concern that the movie succeeds. Given the low success rate, models and mechanisms to predict reliably the ranking and or box office collections of a movie can help de-risk the business significantly and increase average returns. The current predictive models available are based on various factors for assessment of the movie. These include the classical factors such as cast, producer, director etc. or the social factors in form of response of the society on various online platforms. This methodology lacks to harvest the required accuracy level. Our paper suggests that the integration of both the classical and the social factors (anticipation and user feedback) and the study of interrelation among the classical factors will lead to more accuracy.read more
Citations
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Journal ArticleDOI
Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning
TL;DR: This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model that will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.
Journal ArticleDOI
Understanding Videos at Scale: How to Extract Insights for Business Research
TL;DR: This article provides a consolidated tool to efficiently extract 109 video-based variables, requiring no programming knowledge, including structural video characteristics such as colorfulness as well as advanced content-related features such as scene cuts or human face detection.
Proceedings ArticleDOI
Predicting IMDb Ratings of Pre-release Movies with Factorization Machines Using Social Media
TL;DR: This study aims to explore the use of Factorization Machines approach in order to predict movie success by predicting IMDb ratings for newly released movies using social media data and compare it to current studies.
Proceedings ArticleDOI
Mining trailers data from youtube for predicting gross income of movies
TL;DR: A dataset of 7988 movie trailers from YouTube is prepared and it is depicted that linear regression is the most suitable method regarding the prediction of movies gross income using these attributes.
Journal ArticleDOI
Predicting the performance of TV series through textual and network analysis: The case of Big Bang Theory.
TL;DR: In this paper, a well known TV series—The Big Bang Theory—is considered to identify factors leading to its success and two sets of predictors are identified respectively for appreciation and popularity.
References
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Journal ArticleDOI
Predicting box-office success of motion pictures with neural networks
Ramesh Sharda,Dursun Delen +1 more
TL;DR: Comparison of the use of the neural network in predicting the financial performance of a movie at the box-office before its theatrical release to models proposed in the recent literature as well as other statistical techniques using a 10-fold cross validation methodology shows that the neural networks do a much better job of predicting.
Journal ArticleDOI
Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data
Márton Mestyán,Taha Yasseri,Taha Yasseri,Taha Yasseri,János Kertész,János Kertész,János Kertész +6 more
TL;DR: It is shown that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.
Journal ArticleDOI
Predicting movie grosses: Winners and losers, blockbusters and sleepers
TL;DR: In this paper, the authors predict movie grosses: Winners and Losers, Blockbusters and Sleepers, and predict the box office performance of each movie based on its box-office performance.
Proceedings ArticleDOI
Web Science 2.0: Identifying Trends through Semantic Social Network Analysis
TL;DR: A novel set of social network analysis based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new trends to predict long-term trends on the popularity of relevant concepts such as brands, movies, and politicians are introduced.
Proceedings Article
Blogs as Predictors of Movie Success
TL;DR: Analysis of a comprehensive set of features extracted from blogs for prediction of movie sales is presented, using correlation, clustering and time-series analysis to study which features are best predictors.