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The purpose of this paper is to combine basic movie information factors, external factors and review factors, to predict box-office performance and identify the most crucial factor of influence for box-office performance.

Five movie genres and first-week movie reviews found on IMDb were collected. The movie reviews were quantified using sentiment analysis tools SentiStrength and Stanford CoreNLP, in which quantified data were combined with basic movie information and external environment factors to predict movie box-office performance. A movie box-office performance prediction model was then developed using data mining (DM) technologies with M5 model trees (M5P), linear regression (LR) and support vector regression (SVR), after which movie box-office performance predictions were made.

The results of this paper showed that the inclusion of movie reviews generated more accurate prediction results. Concerning movie review-related factors, the one that exhibited the greatest effect on box-office performance was the number of movie reviews made, whereas movie review content only displayed an effect on box-office performance for specific movie genres.

Because this paper collected movie data from the IMDb, the data were limited and primarily consisted of movies released in the USA; data pertaining to less popular movies or those released outside of the USA were, thus, insufficient.

This paper helps to verify whether the consideration of the features extracted from movie reviews can improve the performance of movie box-office.

Through various DM technologies, this paper shows that movie reviews enhanced the accuracy of box-office performance predictions and the content of movie reviews has an effect on box-office performance.

Sequel 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.

A 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.

The 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.

The model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.

Previous 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.

The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India.

This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average.

In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movie Singham Returns over method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores.

The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction.

The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.

Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).

Because interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.

The IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.

Different from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.

The purpose of this paper is to develop a predictive model for box office performance based on the textual information in movie scripts in the green-lighting process of movie production.

The authors use Latent Dirichlet Allocation to determine the hidden textual structure in movie scripts by extracting topic probabilities as predictors for classification. The extracted topic probabilities are used as inputs for the predictive model for the box office performance. For the predictive model, the authors utilize a variety of classification algorithms such as logistic classification, decision trees, random forests, k-nearest neighbor algorithms, support vector machines and artificial neural networks, and compare their relative performances in predicting movies' market performance.

This approach for extracting textual information from movie scripts produces a valuable typology for movies. Moreover, our modeling approach has significant power to predict movie scripts' profitability. It provides a superior prediction performance compared to previous benchmarks, such as that of Eliashberg et al. (2007).

This work contributes to literature on predicting the box office performance in the green-lighting process and literature regarding suggesting models for the idea screening stage in the new product development process. Besides, this is one of the few studies that use movie script data to predict movies' financial performance by proposing an approach to integrate text mining models and machine learning algorithms with movie experts' intuition.

First, the authors’ approach can significantly reduce the financial risk associated with movie production decisions before the pre-production stage. Second, this paper proposes an approach that is applicable at a very early stage of new product development, such as the idea screening stage. The authors also introduce an online-based movie scenario database system that can help movie studios make more systematic and profitable decisions in the green-lighting process. Third, this approach can help movie studios estimate movie scripts' financial value.

This study is one of the few studies to forecast market performance in the green-lighting process.

The motion picture industry is a cultural and creative industry. Unlike its US counterpart, the Chinese motion picture industry is still developing. Therefore, learning from the US market, the purpose of this paper is to analyze the business model of Chinese movies from the perspective of new product diffusion.

Based on 66 movies released in the US and 21 movies released in China, this paper first compares the diffusion curves of Chinese and US movies through the movie life cycle and box office trends. Next, it analyzes the moviegoing behaviors of Chinese and US audiences based on the innovation and imitation coefficients in the Bass model. Finally, it compares the attention to information of Chinese and US audiences from the perspective of interpersonal word-of-mouth (WOM).

In the USA, a movie’s highest weekly box office is usually in its opening week, followed by a weekly decline in revenue; in China, there is no difference in box office performance between the first two weeks, but a weekly decline in revenue similarly follows. US audiences pay more attention to advertisements for movies than WOM recommendations, while Chinese people pay more attention to WOM recommendations. Neither the Chinese nor the US market differs in the volume of WOM between the first week before release and the opening week, and these two weeks are the most active period of WOM in both markets.

During the production phase for Chinese movies, we should satisfy opinion leaders’ needs. During the distribution phase, we should not only focus on market spending before the movie’s release, but also increase market spending in the opening week. During the theater release phase, we should stimulate WOM communication between moviegoers and thereby attract many more opinion seekers.

Few studies have investigated the Chinese motion picture industry from the perspective of new products. This paper compares and analyzes the diffusion of Chinese and US movies using the Bass model of new product diffusion, providing systematic theoretical guidelines for the commercial operation of the Chinese motion picture industry.

This study aims to identify the factors that influence box office performance in the specific context of the adaptation of science fiction (SF) to film in Hollywood.

Fifty-one film adaptation cases were collected and empirically analyzed with two-stage least-squares (2SLS) regression.

Empirical analysis demonstrates that the adaptation of the title, the popularity of the original novel and the director's experience in film adaptation have significant impacts on box office performance.

The study contributes to the literature by bridging the gap between two separate streams of the research literature on film performance and film adaptation. Moreover, the study has extended the literature on the prediction of film performance by examining important factors in the special context of SF film adaptation.

In the case of film adaptation, recruiting an experienced director will be a good choice. Author power is also required for attracting more investment and increasing audience share in the short term. From a marketing perspective, pointing out in the title that the film is an adaptation of an original novel would be an advantageous approach.

This is among the pioneering research related to the effects of film adaptation on box office performance. The approach and results of this study direct future studies in many aspects.

The purpose of this paper is to investigate two questions. First, are movie-based online community evaluations (CE) on par with film expert evaluations of new movies? Second, which group makes more reliable and accurate predictions of movie box office revenues: film reviewers or an online community?

Data were collected from a movie-based online community Fandango for a 16-month period and included all movies released during this time (373 movies). The authors compared film reviewers’ evaluations with the online CE during the first eight weeks of the movie’s release.

The study finds that community members evaluate movies differently than film reviewers. The results also reveal that CE have more predictive power than film reviewers’ evaluations, especially during the opening week of a movie.

The investigated online community is based in the USA, hence the findings are limited to this geographic context.

The main implication is that film studios and movie-goers can rely more on CE than film reviewers’ evaluation for decision making. Online CE can help film studios in negotiating with distributors, theatre owners for the number of screens. Also, community reviews rather than film reviewers’ reviews are looked upon by future movie-goers for movie choice decisions.

The study makes an original contribution to the motion picture performance research as well as to the growing research on online consumer communities by demonstrating the predictive potential of online communities with regards to evaluations of new movies.

This research investigates the impacts of movie consumers' willingness, measured by the number of people who want to watch a movie, on the relationship between event marketing and box office revenue. This study aims to provide marketers with practical event marketing strategies and tactics to improve box office revenue.

Panel data was collected for 1,141 movies released in China from year 2014–2018 for a total of 12 weeks, spanning 4 weeks before and 8 weeks after release. The mediating effect of consumers' willingness on the relationship between event marketing and box office revenue was tested through a stepwise method and the generalized least squares method based on random effects.

Movie consumers' willingness mediates the effect of event marketing and box office revenue. Both movie consumers' willingness and box office revenue follow an inverted U-shaped distribution against the intensity of event marketing. From the second week before release to the first week after release, intensified event marketing enhances box office revenue. Various types and intensities of event marketing should be employed in different periods of time to increase the total box office revenue.

This research ignores the costs of various types of event marketing for different movies. Future research could consider the cost-effectiveness of event marketing.

The findings of this paper provide meaningful insights on event marketing strategies for practitioners.

This study contributes to the field by verifying movie consumers' willingness as a mediator between event marketing and box office revenue. The study also provides empirical evidences on effective types and reasonable intensities of event marketing over the whole lifecycle of movies.

Consumers often search for movie information and purchase tickets on the go. A synopsis is often provided by producers and theatres in mobile apps and websites. However, to the best of the authors’ knowledge, little research has investigated whether the synopsis has an impact on a movie’s box office. This research uses computerized text analysis in examining the influence of linguistic cues of a synopsis on the movie’s financial performance. This paper aims to show that language choice in a synopsis is a significant factor in predicting box office performance.

A total usable sample of 5973 movies was collected using a web crawler. Computerised text analysis using linguistic inquiry and word count was adopted to analyse the movie synopses data. The empirical study comprises two phases. Phase 1 used exploratory factor analysis on 50 per cent of the sample (Sample 1) to establish the dimensionality of psychological processes as reflected in the linguistic expressions. The analysis identified 11 linguistic variables that loaded on four dimensions. The factor structure was replicated on an independent sample (Sample 2) using confirmatory factor analysis. Phase 2 tested the hypotheses using structure equation modelling.

Results show that consistency between movie genres and linguistic cues in a film synopsis promotes movie box office revenue when linguistic cues shown in the synopsis confirm a consumer’s expectancies about a focal movie genre. Conversely, a synopsis reduces the movie box office revenue when the linguistic cues shown disconfirm the genre-based expectancies. These linguistic cues exert similar effects on action and crime films but different effects on comedies and drama films.

It is likely that consumer tastes and linguistic styles of film synopses have evolved over time. As a cross-sectional study, such changes were not taken into consideration in the current research. A longitudinal study in the future can reveal the dynamic relationship between film synopses and audience.

Managerially, the findings show that a synopsis is an effective communication touch point to position a movie. This research provides concrete guidelines in crafting synopses with the “rights words’ aligned with movie-goers’ expectations within each specific genre. Beyond movie consumption, the research findings can be applied to other entertainment products, such as TV series and books.

To our knowledge, this research is the first in studying the linguistic cues in synopses and its relation to box office performance. It addresses this knowledge gap by answering the basic question of whether movie synopses matter. Methodically, the paper marks the first attempt to use the two-step structural equation modelling method on computerised content analysis data.