Role of different factors in predicting movie success
16 Apr 2015-pp 1-4
TL;DR: 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.
Citations
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01 Jan 2017
TL;DR: Several machine learning techniques are presented along with a genetic algorithm to predict the success of a movie before its production using the IMDB rating as an indicator of the success.
Abstract: Movie production is one of the most expensive investment fields and can result in enormous financial profit or loss. It is critical for investors and production companies to decide whether to invest in a certain movie given the huge loss that could occur from such investments. Hence, it is very beneficial to construct a model which helps investors in their decision making process. Machine learning has proven its effectiveness in building decision making models and recommender systems in various fields. In this work, we present several machine learning techniques (Support Vectors Machine, K-Nearest Neighbors, C5, Neural Networks and Case-Based Reasoning) along with a genetic algorithm to predict the success of a movie before its production using the IMDB rating as an indicator of the success. Results show that machine learning is useful in this domain and genetic algorithms can be used to build prediction models with relatively good performance.
1 citations
01 Jan 2013
TL;DR: Konstanz Information Miner is a open-source tool, developed at the University of Konstanz, containing different Data Mining techniques and the possibility to extract trained models in the PMML (Predictive Model Markup Language) format in order to import them into other applications.
Abstract: VISONE (Visual Social Networks) is an open-source tool, developed at the University of Konstanz, to analyse and visualize graph structures. KNIME (Konstanz Information Miner) is a open-source tool, developed at the University of Konstanz, containing different Data Mining techniques and the possibility to extract trained models in the PMML (Predictive Model Markup Language) format in order to import them into other applications.
1 citations
DOI•
29 Sep 2021TL;DR: In this article, a Long Short-Term Memory (LSTM) and ensemble based approach was proposed to predict the success of movies using metadata and social media, which was able to obtain 81.2% accuracy and outperformed the other implemented models.
Abstract: Twitter, for example, offers a wealth of information on people's choices. Because of social media's growing acceptability and popularity, extracting information from data produced on social media has emerged as a prominent study issue. These massive amounts of data are used to build models that anticipate behavior and trends. On Twitter, people express their opinions regarding movies. In this study, a Long Short-Term Memory (LSTM) and ensemble based approach was proposed predicting the success of movies using metadata and social media. In this research, both social media data and movie metadata were consumed to predict the success of the movies. The metadata of the movie also plays an important role, which can be utilized to predict the success of the movies. IMDb ratings, the genre of the movies, and details about the awards that the movies won or nominated are some of the metadata used in addition to the tweets. LSTM, a neural network (NN) model, was applied to identify the sentiment value of the Twitter posts. Then, the ensemble approach was employed to predict the success of movies using movie metadata and results from the LSTM based NN model. This combined model was able to obtain 81.2% accuracy and outperformed the other implemented models.
1 citations
16 Jan 2019
TL;DR: An empirical analysis by using 13 indexes affecting the movie box office to construct movie box-Office forecast model as well as analyze the principles and the construction steps of the models shows that the partial least squares model has great skills to demonstrate the prediction of results in accurate and fashioned way.
Abstract: Every year, billions of films appear in the box office of the mainland but there is small statistics of sample data for them. There are numerous factors responsible for it e.g. complex, variable box office elements and low accuracy of box office demand forecasting. Whereas, partial least squares regression model has the capability to deal with small sample data and variable multiple correlations. This paper has conducted an empirical analysis by using 13 indexes affecting the movie box office to construct movie box-Office forecast model as well as analyze the principles and the construction steps of the models. The model has utility with respects to process and model accuracy. The empirical results show that the absolute relative error of the partial least squares regression model is 26.6%, the goodness of fit is 87.7%. It shows that the partial least squares model has great skills to demonstrate the prediction of results in accurate and fashioned way.
1 citations
01 Aug 2022
TL;DR: In this paper , the authors evaluate the impact of movie summaries on box office sales and find that movies with negative or neutral summaries achieved higher box office revenue than those with positive summaries.
Abstract: In this paper, we evaluate the impact of movie summaries on box office sales. These summaries are provided by studios as one of the many artifacts used to market a movie to the public. We acquired a set of nearly 74 thousand summaries from English-language films released between 1960 and 2019. We analyzed summary sentiment, length, and vocabulary and compared these metrics to box office sales, studying trends across genres. We observed that movies with negative or neutral summaries achieved greater box office sales than those with positive summaries. While initially surprising, these results correlate to endorphin production caused by negative or traumatic experiences. We also noted that summaries with an average length of 30 words performed better than those with fewer or more words. Finally, we noted that movies with summaries that have a vocabulary level of a college graduate performed better than others.
References
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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.
Abstract: Predicting box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, the use of neural networks in predicting the financial performance of a movie at the box-office before its theatrical release is explored. In our model, the forecasting problem is converted into a classification problem-rather than forecasting the point estimate of box-office receipts, a movie based on its box-office receipts in one of nine categories is classified, ranging from a 'flop' to a 'blockbuster.' Because our model is designed to predict the expected revenue range of a movie before its theatrical release, it can be used as a powerful decision aid by studios, distributors, and exhibitors. Our prediction results is presented using two performance measures: average percent success rate of classifying a movie's success exactly, or within one class of its actual performance. Comparison of our neural network 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 in this setting.
291 citations
"Role of different factors in predic..." refers background in this paper
...LITERATURE SURVEY Though there are many factors that constitute a movie’s success, and it is not always clear how they interact, this paper attempts to determine these factors through the different attributes, social media etc.and predictive analytics....
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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.
Abstract: Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show 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.
281 citations
"Role of different factors in predic..." refers methods in this paper
...Four different regression techniques, support vector regression, Ada boosted decision tree regression, gradient boosting regression and random forest regression were used in this project....
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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.
Abstract: (2000). Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers. CHANCE: Vol. 13, No. 3, pp. 15-24.
164 citations
29 Aug 2009
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.
Abstract: We introduce 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. These algorithms have been implemented in Condor, a software system for predictive search and analysis of the Web and especially social networks. Algorithms include the temporal computation of network centrality measures, the visualization of social networks as Cybermaps, a semantic process of mining and analyzing large amounts of text based on social network analysis, and sentiment analysis and information filtering methods. The temporal calculation of betweenness of concepts permits to extract and predict long-term trends on the popularity of relevant concepts such as brands, movies, and politicians. We illustrate our approach by qualitatively comparing Web buzz and our Web betweenness for the 2008 US presidential elections, as well as correlating the Web buzz index with share prices.
126 citations
Additional excerpts
...In Naive Bayes algorithm, they represented movie as independent combination of associated personas and attributes [2],which was given by, P(rating | movie) proportional to P(movie | rating) * P(rating) ,where P(movie | rating) is product of individual conditional probabilities for each persona....
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Proceedings Article•
20 Mar 2009TL;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.
Abstract: Analysis of a comprehensive set of features extracted from blogs for prediction of movie sales is presented. We use correlation, clustering and time-series analysis to study which features are best predictors.
65 citations
"Role of different factors in predic..." refers background in this paper
...For example , Sharda and Delen have trained a neural network to process pre-release data, such as quality and popularity variables, and classify movies into nine categories according to their anticipated income, from ‘‘flop’’ to ‘‘blockbuster’’ [9]....
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