Movie Box Office Research Articles

Predicting Movie Box Office Based on Machine Learning, Deep Learning, and Statistical Methods

Abstract. The global film industry has been proved to impose a significant impact on both culture and the economy, with box office revenue serving as a crucial indicator of a film's commercial success. This study utilizes data from the Kaggle "TMDB Box Office Prediction" competition, encompassing 3,000 films released between 1990 and 2018, to predict movie box office revenue using Random Forest, XGBoost algorithms, and deep learning models such as Bidirectional Long Short-Term Memory (Bidirectional LSTM) and Simple Recurrent Neural Network (SimpleRNN). The goal is to develop a model that accurately predicts movie box office. By comprehensively considering multiple factors such as budget, popularity and film characteristics, this study not only significantly improves the accuracy of box office prediction, but also provides a scientific basis for the formulation of film market strategies. The results demonstrate that the Bidirectional LSTM excels in handling complex time-series data, showing strong trend-capturing capabilities, while XGBoost exhibits greater robustness in dealing with complex data and outliers. These findings can not only provide guidance for making more effective strategies on film production and distribution, but also provide new directions for future research, such as delving into the impact of social media on box office and developing more sophisticated predictive models to adapt to changing market dynamics.

Prediction techniques of movie box office using neural networks and emotional mining

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved.

BarbieThe Combination of Feminism and Commercial Film

With the continuous development of feminism in recent years, there have been many new manifestations in the field of culture and art. In 2023, the movie Barbie was released, which became a phenomenon of feminist artworks in the new era and triggered a wide discussion in society. This study explores the feminism embodied in Barbie through the analysis of the contents and characters in the movie. It provides new ideas and references for the combination of feminism and commercial film. By combining feminist and commercial films, Barbie has managed to spark a big debate on social issues while claiming the top spot at the 2023 box office. In terms of movie content, movie box office, movie reputation and so on, the movie Barbie is a very successful work. The success of Barbie undoubtedly provides a very new and good reference for the development of feminism in literary and artistic works. In today's still patriarchal world, feminism wants to have a better development in literary and artistic works, and the combination of feminism and commercial film models is a very effective means.

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

Predicting the revenue of a movie prior to its release presents a significant challenge. The ability to predict pre-release revenue enables movie production companies to devise effective marketing strategies and mitigate the risks associated with potential box office failures. The primary hurdles in this endeavor stem from managing the myriad factors influencing box office outcomes and accurately forecasting a movie's revenue before it becomes available to the public. To overcome these challenges, we introduce a sophisticated Early Movie Box Office Prediction Model that incorporates Ensemble Learning and Feature Encoding techniques. This model amalgamates multiple foundational models, utilizing regression and decision trees to forecast box office revenues. Our composite model demonstrates superior performance over its constituent models, achieving an impressive accuracy rate of 91.4%.

Long–Range Movie Box Office Prediction Based on Machine Learning

The study aims to predict the box office of movies based on several important factors obtained from The Movie Data Base (TMDB) being independent of the daily box office of the movie after its release. Exploring the nature of the dynamics of film revenue is crucial for filmmakers, investors, and film likers since it can help them find a way to improve their film influences and decision-making. This research employs three distinct methods – Random Forest, Back Propagation Neural Network, and Linear regression (Least Square Method) – utilizing a set of selected independent variables including popularity, budget, genre, run time, release date, original language, and production countries. This study gives an insight into the relationships between these variables and the revenue of movies. As a long-range forecasting model, the prediction of the model is up to 73% precise. Overall, this study aims to provide valuable information and methods for the film-related industry to predict revenue. Also, the model has the potential to be extended to other fields such as the prediction of the feedback of series or games before release.

A false emotion opinion target extraction model with two stage BERT and background information fusion

As social media has gradually become an indispensable part of people’s life, more and more users begin to express their opinions on social media. These opinions contain rich emotional information, as well as many abnormal false emotion. And the opinion target rich in false emotion will have great potential value in the field of movie box office prediction, public opinion guidance and so on. Therefore, we propose a false emotion opinion target extraction task and two stage BERT+CRF model based on the concept of false emotion. Firstly, two stage BERT network includes C-BERT and W-BERT. We use C-BERT to learn the users comment feature, and W-BERT to learn the background content features of microblog. Then, we design five feature interaction fusion methods to fuse the features learned by C-BERT and W-BERT. At last, we transform the false emotion opinion target extraction task into a sequence labeling task, and use the CRF method to learn the label dependency of the context and output the optimal sequence among all candidate sequences. Due to the lack of false emotion opinion target datasets, we collate a false emotion opinion target dataset Weibo23. Comparing with the advanced models used in related research, including some sequence labeling models and named entity recognition models, two stage BERT+CRF model achieves an F1 score of 0.8834, which is about 10%–20% higher.

Research on the prediction method of first-day box office considering the holiday factor

The prediction of first-day box office has been a hot research topic. However, existing research on movie prediction models lacks analysis and exploration of the holiday factor. In order to uncover the holiday factor contributing to the fluctuation of movie box office, we propose a prediction method for the first-day box office considering the Holiday Factor. Initially, we present a SARIMABox-Cox model for time series analysis. The experimental results reveal significant differences in movie box office during weekends and weekdays, as well as during summer vacation and non-summer vacation periods. Consequently, we introduce two variables, weekend dummy variable and summer vacation dummy variable, as holiday factors. Subsequently, to further investigate the impact of the holiday factor on the accuracy of first-day box office prediction, we integrate the holiday factor with multiple linear regression (MLR), random forest (RF), support vector machine regression (SVR), deep neural network (DNN), and extreme gradient boosting (XGBoost) to establish several first-day box office prediction models. Experimental results on the publicly available dataset Daily Earnings Data for 3400 Movies indicate a substantial improvement in the performance of models considering the holiday factor, with the highest enhancement reaching 16.42%.

Assessment of Educational Digital Game-Based Learning and Academic Performance of Grade Six Pupils

Digital games have surpassed the movie box office and are making their way to revolutionize the contemporary teaching methods of today’s classrooms. Educators, parents, and policymakers are debating its effect on the students, and more researchers are offshooting to prove its effectiveness and suitability for the recent digital natives. This study, therefore, found empirical evidence underpinning the efficacy of Educational Digital Game Learning on the learners’ academic performance, specifically in English concepts, using a quasi-experimental control group and descriptive design. Two classes of grade VI students were involved and labeled as the control and experimental group. During the study, the researcher used a validated test instrument. The researcher also filmed how EDGBL was conducted in the classroom and recorded interviews with experienced students. Frequency counts, percentages, weighted mean, and t-tests for independent samples were used to investigate the aims and establish the results. The findings revealed that students who underwent Educational Digital Game-Based Learning had improved their academic performance in learning English concepts. It was also found that those students who were exposed to EDGBL developed more positive habits in learning English. In contrast, those exposed to the traditional method tended to misbehave and have a monotonous learning environment. Moreover, conducting EDGBL as a method requires teachers to have practical facilitating skills to maintain solid work coordination throughout the implementation of EDGBL.

Discovering the relationship between the number of film review topics and box office with NLP techniques

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website maoyan.www, and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work.

基于预训练–微调策略的电影票房预测

基于预训练–微调策略的电影票房预测

Pengaruh Social Media dalam Membentuk Loyalitas Terhadap Film di Bioskop dengan Mediasi Movie Coolness serta Pengalaman Menonton Film

In 2022 the Indonesian film industry experienced a revival after two years of being affected by the COVID-19 pandemic. Restrictions imposed during pandemic and the changes of consumer behavior have increased the reliance on social media as information provision channel. The phenomenon also transpires in the practice of movie marketing. The purpose of this study is to examine the utilisation of social media as a movie marketing tool by investigating the mediating effects of movie coolness and cinematic experience on movie loyalty. Moreover, this study focuses on the recent Indonesian highest box office movie, KKN in Dancer Village movie. In doing so, this study integrate a framework of social media information dimensions (i.e., interestingness, value added, information amount, virtual appearance) and social media contents (i.e., advertising and promotion), and analyse their impacts on movie coolness, movie experience, and movie loyalty. This study collected 236 respondents and analyzed the data using SEM (Structural Equation Modeling) using SmartPLS 4.0. The finding suggest that the amount of information and social media content are significant drives of movie experience which lead to a loyaltu to a movie. The results of this study can provide insights to better plan marketing activities of Indonesia movies. In addition, this research contributes to the progress of literature in social media marketing and movie marketing.

거리두기 해제 이후 상영작 흥행 요인 분석

This study empirically analyzed factors affecting box office success, focusing on films released after lifting COVID-19 social distancing. T-test for the comparison of mean values and multiple regression analysis were conducted using domestic theater data for each day of the week, focusing on concert movies. In addition, we analyzed factors affecting the success of theater movies, such as special screenings and screening days. The results of the empirical analysis are summarized as follows. First, screenings in the form of special theaters focusing on Screen X and 4D recorded relatively low screen share, but recorded high ticket sales revenue. Second, special event screenings, such as lightstick screenings or sing-along screenings, were analyzed as a major factor affecting movie box office success. It was found that special screenings performed better in terms of box office compared to regular screenings. Third, box office performance on Wednesday as well as on weekends was estimated to be higher than on Monday. This study contributed to the literature by analyzing both the number of movie audiences and movie ticket sales as indicators of a movie's box office performance and deriving empirical findings.

The Influence of Film Marketing Strategy on the Film Box Office

The purpose of this study is to have a more in-depth study and discussion on marketing and film marketing market, so as to provide more thinking and suggestions for China's future film marketing strategies, promote the development of Chinese film marketing and make Chinese films better go to the world. This paper used the analysis of the box office data to compare the box office differences between Chinese films and foreign films (China and the United States, China, and Western films) and analyze the influence of different groups on the box office of Chinese films. From the research conclusion: the selection of material can greatly affect the movie box office. Through the analysis of the performances of patriotic films and science fiction films in domestic and foreign markets, it’s can see that patriotic films are more dominant in China, and the box office of science fiction films is also growing rapidly in recent years. Through the analysis of different movie publicity strategies, it can be concluded that the star effect has both positive and negative effects. It can better guarantee the support rate of fans for movie stars, but in some movies, it also damages the attraction of the movie to the general audience to a certain extent. Through the study of word-of-mouth marketing, this paper found that the influence of word-of-mouth marketing is decreasing because of the increasing number of false word-of-mouth publicity and false comments.

Decomposition-integration-based prediction study on the development trend of film industry

Movies have the unique ability to both generate income and spread culture, thus predicting the direction of the film industry's growth has garnered a lot of interest. Given the volatility of the movie industry's entire box office revenue dataset and the peculiarities of tiny samples, this article incorporates the decomposition-integration notion to build the EEMD-PSO-LSSVM model movie box office prediction model. The historical box office data are first broken down into many components using the ensemble empirical modal decomposition technique, and then, distinct sequences are predicted using the least squares support vector machine prediction method with particle swarm optimization, and ultimately, the predictions for each sequence are combined. The experimental results demonstrate the effectiveness of the decomposition-integration technique in illustrating the data fluctuation characteristics of quarterly movie box office revenues. When compared to other models, the model proposed in this study has clear advantages in the problem of predicting the time series data of box office revenues that are non-linear, non-smooth, and non-large samples.

How Long Will It Delay?

In the growth process of movie IWOM, the antecedent IWOM has a significant influence on the subsequent IWOM. IWOM does not form all at once, but iteratively over a short period. This article explores the influence of IWOM publishers on IWOM growth and the dynamic impact of IWOM on movie box office by using vector autoregressive model (VAR model) and impulse response analysis. The findings reveal that highly influential and active users' statements stimulate discussion enthusiasm and increase related topic discussions. These statements also reduce the discreteness of IWOM. On the other hand, highly professional users make IWOM more discreet. Both increased discussion enthusiasm and differentiated IWOM contribute to the growth of movie box office. Additionally, during the growth of IWOM, there is an approximately five day “advance period of word-of-mouth regeneration”: it takes audiences about three days from reading movie reviews to watching a movie, followed by about two days to write their own reviews, and the whole process takes about five days.

Promosi Pariwisata Bali dan Queensland melalui Film “Ticket to Paradise”

The film industry and tourism have a very strong relationship, which can benefit one another. Various tourist destinations use films to promote their area and attract tourists. There are also many films made in tourism destinations and showcase these destinations, so as to attract tourists to visit them. As a world popular destination, Bali has been choosen by several film producers to make film here such as the international box office movie “Eat Pray Love” (2010) and the most recent one is “Ticket to Paradise” (2022). The story of “Ticket to Paradise” sets in Bali but did not shoot here, instead in Queensland Australia because of the Pandemic Covid-19. This study aims to analyze the benefits of film "Ticket to Paradise" for Bali and Queensland tourism tourism industry The analysis was carried out using interpretive qualitative methods in investigating the tourism symbols contained in the film. In terms of the storyline, dialogue and visuals contained in this film, this study concludes that the film "Ticket to Paradise" both promotes the tourism industry in Bali and Queensland, although the promotional impact will be more optimal if the film is made and shot in Bali.

The dynamic impact of electronic word‐of mouth on movie box office sales: Based on innovation diffusion theory

AbstractElectronic word of mouth (eWOM) is one of the most influential forms of information and strongly affects customers' willingness to adopt new innovations. Using four metrics of eWOM from YouTube—the numbers of likes, dislikes, views, and comments—this study empirically investigates the dynamic relationships between eWOM and movie box office sales based on innovation diffusion theory. The results show that the innovation diffusion process is applicable in the movie industry. That is, the impacts of the number of views, likes, dislikes, and comments for movie trailers on YouTube on movie box office revenue are first increasing and then gradually decreasing.

A movie box office revenue prediction model based on deep multimodal features

A movie box office revenue prediction model based on deep multimodal features

유통배급단계에서의 영화흥행예측

Most movie box office predictive models use various variables related to movie production, audience, and movie screening. However, one of the most important variables in the distribution stage when a movie is released is the movie review related to the audience, that is the subject of word of mouth. In most predictive models in which various variables have been used so far, movie reviews have been substituted into emotional indices for additional analysis. In this case, it can be said that it has a limitation in that it is difficult to specifically grasp the contents of word of mouth about the movie. In this paper, only words constituting movie reviews on online portal sites were used as independent variables in the movie box office predictive model, and the relationship with movie box office performance was identified using the LASSO model. Through this, the predictive power of movie reviews on movie box office success was evaluated, and the words in movie reviews that affect movie box office were investigated. As a result of the analysis, the LASSO regression coefficients of words had a correlation with the success of the movies. The LASSO regression coefficients increased as the words appeared in reviews of successful movies, but decreased as they appeared in reviews of unsuccessful movies. As a result, it can be said that movie box office prediction using movie reviews can be helpful for planning additional investment strategy at the distribution stage.

Analysis of the influence factors of global film box office based on a log-linear model

In this paper, python is used to obtain the data of the top 100 global movie ticket rooms, and through data visualization, it is concluded that the global movie box office will peak in the next 10 years. Science fiction action movies are more popular. r-language was used to construct log-linear models, respectively. The results of the random unit group design model showed that science fiction action movies were more favorable to the box office. Through k-means clustering analysis, it was concluded that the guidance of well-known directors had more positive effects on the box office. According to the above conclusions, the major cinemas and film and television companies make relevant suggestions.