Movie Success Research Articles

Exploring the key success factors of films: a survival analysis approach

This paper investigates the key factors that contribute to the success of movies. By using sentiment and survival analysis, this study classified 1,038 movies according to the customer comments and movie characteristics and compared the number of screening days, the primary measure of success of movies, between the groups. Based on the analysis of film reviews (i.e., positive, negative, and neutral), screening days showed significant differences between (1) the positive and neutral groups, negative and neutral groups, (2) the density (|positive—negative comments|) of the positive and negative groups, (3) drama and action, drama and comedy, (4) domestic and foreign films, (5) G-Rated and R-Rated, R-Rated and X-rated films.

Different Cinematic Interpretations of Ch’unhyangjŏn: The Same Korean Identity

The article examines the classic Korean folklore fable, 춘향전 (春香傳), Ch’unhyangjŏn (The Fragrance of Spring), The Tale of Ch’unhyang, through the lens of three different successful movie adaptations produced in North and South Korea. Respectively, Yu Wŏn-chun and Yun Ryong-gyu portrayed The Tale of Ch’unhyang (1980) in its modest “Juche realist” North Korean film style, whereas Im Kwŏn-t’aek depicted his work, Ch’unhyang (2000), in a contemporary liberally and daringly revised version, while the romantic portrayal produced in North Korea by the South Korean film director, Shin Sang-ok, in Love, Love, My Love (1984), is performed from a human-oriented and entertaining perspective filled with musical ingredients and brave images of love. The study aims to demonstrate how the story is diversely interpreted through the two divided film cultures by highlighting differences between collectivism and individualism, noting also that all three interpretations emerge from similar roots of cultural and national identity.

Movie Success Prediction Using Data Mining

The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.

Prediction of Box Office for Bollywood Movies Using State-of-the-Art SentiDraw Lexicon for Twitter Analysis

Films are a high-risk industry. Accurate prediction of movie box-office revenues can reduce this market risk and inform the investment decisions regarding promotion of the movie closer to a film’s release or right after release. Studies have shown that chatter on social media platforms like Twitter along with certain movie-related factors can be useful in predicting success of movies. Sentiment of tweets for any movie gives important information about the consumer’s reaction and the polarity of these sentiments has been shown to have an impact on prediction of box-office revenues. This paper presented a novel Bollywood domain specific sentiment lexicon that delivered state-of-the-art performance for polarity determination of reviews. SentiDraw lexicon was built on movie reviews scraped from IMDB and calculated the sentiment orientation of these words by calculating the probability distribution of words across reviews with different star ratings. The results showed that SentiDraw lexicon delivered a superior performance compared to any other lexicon-based method. This significantly contributed in enhancing the prediction accuracy of box office for movies using textual data from Twitter for analysis. In fact, this study demonstrated an extremely parsimonious regression model that used only budget, hype factor, tweet volume, and polarity of tweets for a robust prediction of box office revenues even before the release of a movie.

극장과 VOD 흥행에서 홀드백을 포함한 영화 흥행 요인의 영향

Research on movie revenue has been divided into analysis of traditional box-office and studies on online market such as VOD, which have recently emerged. However, the commercial success of a particular movie consists of the box-office revenue and the secondary market performance including VOD. The box-office and VOD markets are connected by holdback, which is the time difference of movie and VOD release, and hence the structured relationship should be considered. In this study, a path analysis model was established and analyzed in order to compare the effects of various factors for the box-office revenue and VOD downloads including the direct and indirect effects of holdback. As a result of an empirical analysis of 237 movies released from November 2018 to October 2019, the factors that mainly influence the box-office success of a movie are marketing and distribution characteristics such as expert ratings and the number of screens, and those for VOD success are creative characteristics such as movie rating and star power. In terms of genre, it was found that drama is advantageous for box-office and action genre for VOD. It was confirmed that holdback tends to directly or indirectly increase box-office revenue and also decrease VOD downloads. The results of this study can have various implications for effective operation strategies and additional copyright contracts including holdbacks for film producers, distributors, and other secondary market stakeholders.

Analyzing and Predicting The Success of Box Office Collection of a Movie Using Machine Learning

Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies are available over the internet. The framework will foresee an estimated achievement pace of a film dependent on its productivity by dissecting verifiable information from various sources like IMDB, Rotten Tomato, Box Office Mojo and Metacritic. Utilizing distinctive AI calculations, Machine Learning Tools, and different procedures the framework will foresee a film box office benefits depending on certain highlights like caste, genre, budget, actors, and many more features. The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. Thus, predicting the box-office will help this growing industry experts to imply some important business decisions in order to make the upcoming movie more successful.

The ramifications of employing artificial intelligence and advanced technologies in the film industry

AI refers to Artificial intelligence which is a current computing technology that provides a way to interact with machines and propose human interaction systems. AI-based systems evinced to be capable of proposing automated systems by which business operations and activities can be performed automatically without requiring human assistance. The film industry is a leading sector where AI technology is implemented, enabling editors to create effective and significant movies using human-based systems and programs. The aim of this research is to review the implication of AI and amalgamate on the future of the film industry by reviewing the benefits and challenges related to AI-based systems and technologies. A literature review is included to review the findings of recent papers and to gain effective facts and information related to AI in the film industry. It is found that with the help of AI-based facial recognition programs, editors can examine the major characters and sort out major scenes for human editors effectively. Numerous film industry areas where AI technologies can be applied include writing scripts, predicting movie success, selecting actors, promoting movies and many more. In the future, the film industry will be dependent on AI technologies and machine learning programs where editing and promotion-related activities will be done effectively. This research highlights the implications of AI technology on the film industry and deepens our understanding and knowledge for the field of AI technologies.

The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success

There exists significant hype regarding how much machine learning and incorporating social media data can improve forecast accuracy in commercial applications. To assess if the hype is warranted, we use data from the film industry in simulation experiments that contrast econometric approaches with tools from the predictive analytics literature. Further, we propose new strategies that combine elements from each literature in a bid to capture richer patterns of heterogeneity in the underlying relationship governing revenue. Our results demonstrate the importance of social media data and value from hybrid strategies that combine econometrics and machine learning when conducting forecasts with new big data sources. Specifically, although both least squares support vector regression and recursive partitioning strategies greatly outperform dimension reduction strategies and traditional econometrics approaches in forecast accuracy, there are further significant gains from using hybrid approaches. Further, Monte Carlo experiments demonstrate that these benefits arise from the significant heterogeneity in how social media measures and other film characteristics influence box office outcomes. This paper was accepted by J. George Shanthikumar, big data analytics.

Analysing Movie Success Based on Machine Learning Algorithm

The Film Industry is producing several hundreds of movies paving the way for the United States for third position in the list of giants of film Industry of the whole world [1]. The expenditure on these movies are of the ten to eleven figures or we can say thousands of millions of dollars, which ensure their box office success, is absolutely essential for the survival of the industry. Predicting what sort of movies are going to earn more and what type is going to come up short before the release, may profit the houses extraordinarily because it will also empower them to work on their promoting efforts which itself need numerous dollars, appropriately[2]. Furthermore, it would likewise assist them with knowing when it is generally suitable to deliver a film by taking a look at the general market. Along these lines, the examination of film achievement is critical to the business. ML (Machine Learning) algorithms [3] calculations broadly need to make expectations like development inside the stock trade, interest for items, nature of tumors, and so on.

Anna Karenina and the two envelopes problem

SummaryThe Anna Karenina principle is named after the opening sentence in the eponymous novel: Happy families are all alike; every unhappy family is unhappy in its own way. The two envelopes problem (TEP) is a much‐studied paradox in probability theory, mathematical economics, logic and philosophy. Time and again a new analysis is published in which an author claims finally to explain what actually goes wrong in this paradox. Each author (the present author included) emphasises what is new in their approach and concludes that earlier approaches did not get to the root of the matter. We observe that though a logical argument is only correct if every step is correct, an apparently logical argument which goes astray can be thought of as going astray at different places. This leads to a comparison between the literature on TEP and a successful movie franchise: it generates a succession of sequels, and even prequels, each with a different director who approaches the same basic premise in a personal way. We survey resolutions in the literature with a view to synthesis, correct common errors, and give a new theorem on order properties of an exchangeable pair of random variables, at the heart of most TEP variants and interpretations. A theorem on asymptotic independence between the amount in your envelope and the question whether it is smaller or larger shows that the pathological situation of improper priors or infinite expectation values has consequences as we merely approach such a situation.

Social media-driven customer engagement and movie performance: Theory and empirical evidence

Can social media-driven customer engagement help movie producers to increase movie sales? Movie producers use social media to boost customer engagement; however, our understanding of whether there is a relationship between pre-consumption customer engagement and movie sales is limited. This research explores what drives the success of movies on the opening-weekend. We examine the relationship between pre-consumption customer engagement driven by social media (i.e., personal and interactive engagement) and movie performance (i.e., opening-weekend box office revenues). Driven by the debate about the potential capacity of social media-driven customer engagement to improve movie performance and the limited understanding of the association of pre-consumption customer engagement and movie performance, this research suggests that social media-enabled customer engagement (i.e., personal and interactive engagement) has the potential to improve movie performance, and these two types of engagement can interact to make this effect stronger. We analyze these relationships drawing from the uses and gratifications theory and the social media affordances perspective that motivate customers to interact in social media. The theoretical model evaluation was tested on a unique and original dataset composed of 966 movies released in the U.K. and Spain. The empirical analysis suggests that personal and interactive engagement are positively related to movie performance, and the positive effects of personal and interactive engagement on movie performance are mutually reinforcing. This research contributes to Information Systems (IS) research by theorizing and empirically showing how customer engagement driven by social media creates business value by improving movie performance.

Movie Title Keywords: A Text Mining and Exploratory Factor Analysis of Popular Movies in the United States and China

Unprecedented opportunities have been brought by advancements in machine learning in the prediction of the economic success of movies. The analysis of movie title keywords is one promising but rarely investigated direction of study. To address this gap, we performed a text mining and exploratory factor analysis (EFA) of the relationships between movie titles and their corresponding movies’ levels of success. Specifically, intragroup and intergroup analyses of 217 top hit movies in the United States and 245 top hit movies in China showed that successful movies in these two major movie markets with outstanding total lifetime grosses featured titles with similar and different patterns of most frequently used words, revealing useful information about viewers’ preferences in these countries. The findings of this study will serve to better inform the movie industry in giving more economically promising names to their products from a machine-learning perspective and inspire further studies.

The Cognitive Interpretation of Multimodal Metaphor in the Movie “Mr. Six” under the Background of Computer Digital Media Art

Under the background of computer digital media art, the film industry has exploded rapidly. “Mr. Six” is a film directed by Guan Hu and starred by Feng Xiaogang, Xu Qing, etc., which was released in China on December 24, 2015. The movie “Mr. Six” is a painstaking narrative film, which creates a new record of movie public praise with its unique interpretation and expression. Therefore, the movie “Mr. Six” is a successful movie, which deserves people’s praise and research, especially the multimodal metaphor in the movie. Firstly, this paper analyzes the metaphorical aesthetics in movies. Then, this paper analyzes the montage technique of metaphor expression. Finally, this paper puts forward the important role of metaphor.

Why Do Cash Cow Movies Work?

This research study aims to investigate all possible reasons that lead to the success of Cash Cow movies in Hollywood. We examine diverse elements used to produce these movies to identify the key factors that ensure the consistent success of cash cow movies since the beginning. It is evident from the box-office records that these movies have tested the tides of time yet have always been in public demand for decades. This study also examines why movie critics have hardly applauded these commercially hit movies as the jury never nominates them for any significant accolades, including Academy or Oscar Awards. Further, this research analyzes the duration of the success of cash cow movies. As the Top Lifetime Grosses (2020) indicate, cash cow movies have almost always been stood out as the 'highest-grossing movies of the year for the past two decades, and they barely suffer box office fatigue for long. While examining these diverse aspects, we also try to determine the dynamic effects these movies have created on the global cinema industry regarding the nature and quality of content being created and the revenue generated.

A hybrid ensemble machine learning model to predict success of Bollywood movies

Bollywood is a multi-billion industry. Hundreds of films are released every year, where each film is an investment of multi-crores. In terms of awards or marketing it has found a place in almost every country and culture. It also contributes and attracts skilled and passionate people to become entrepreneurs. Therefore, it becomes a need as well as a huge concern of the director, producer and all stakeholders involved in a particular film to know the chances of the success of a film on the box office before its release. To address this concern, a hybrid ensemble machine learning model has been proposed. The model uses data sets collected from various sources, such as Boxofficeindia, cinemalytics, YouTube, etc. The model performed pre-processing on data set, which included handling of missing values with mean, cleaning of data, and removal of text values. Feature engineering has been applied in the model to create a new feature called act_direct to make the model more robust. Further, the effectiveness of the model has been tested in terms of accuracy and the AUC-ROC curve. From the experimental results, it is evident that the proposed model ensures relatively better accuracy compared to some recent state-of-art models.

A hybrid ensemble machine learning model to predict success of Bollywood movies

A hybrid ensemble machine learning model to predict success of Bollywood movies

Magia, religión y mito. Harry Potter bajo el signo de la posmodernidad

On the one hand, our aim with this paper is try to analyse how Harry Potter stories were built under postmodern narrative patterns in which J.K. Rowling is a late and elusive example. On the other hand, we reflect on how this creative literature process proposes the combination and reformulation of several references of the Western Culture: religious and mythical, artistic and all those related to popular tradition. All this was taken into account when the books metamorphosed into the successful movie saga.

Family Values in the Movie “A Quiet Place”: A Semiotic Approach

There are only few commercially successful horror movies over fifty years since 1970s that win many awards. One of many horror films released in 2018 that left deep impression for audiences is “A Quiet Place”. McClintock (2018) claimed that the movie has earned more than $300M globally, making strides toward becoming one of the biggest-grossing original scary movies ever. “A Quiet Place” is a silence movie directed by John Krasinski. Because of its silence and quietness, the audiences were forced to focus on the nonverbal signs in the movie. The theme of this movie was family values. One of many important films is mise –en-scene as this communicates indirectly to the audiences. The aim of this study was to know how the theme “Family Values” was visualized in the movie. Using qualitative approach and Semiotics analysis method, this study focused on how the signs and mise-en-scene in the film were interpreted based on the researchers’ interpretation using Metz’ Grand Syntagmatique (1974). This study was a desk research and the data were obtained from selected scenes of the movie. The results showed that there were five syntagma category used in the film; i.e. autonomous shot, episodic sequence, scene, alternate syntagma, and descriptive syntagma. The signs that were presented in the film were arranged by using those five syntagma to show the audience about the family value in the movie.

Sequel movie revenue prediction model based on sentiment analysis

PurposeSequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.Design/methodology/approachA sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.FindingsThe results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.Research limitations/implicationsThe model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.Originality/valuePrevious studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.

A Parsimonious Predictive Model of Movie Performance

In this paper, the authors develop a parsimonious model that offers early prediction of potential success of a movie. In order to achieve this, a broad look at the drivers of movie success is required. Supply chain members will be making decisions regarding movie popularity with regard to licensing contracts, forecasting toy purchases, cross-promotions, etc. at varying times before a movie is released. A simple forecasting approach using publicly available data could improve supply chain decision making. Prior literature suggested that the virtual movie stock market, HSX, was a good predictor. Using a small set of variables including view counts, likes, and dislikes did offer some predictive value. However, HSX produces a forecast that dominates prior models while using a single readily available public data. Further, the HSX-based prediction showed consistency and convergence across a significant breadth of time.