Predicting movie Box-office revenues by exploiting large-scale social media content
TL;DR: The experimental results show that large-scale social media content is correlated withMovie box-office revenues and that the purchase intention of users can lead to more accurate movie box- office revenue predictions.
Abstract: Predicting the box-office revenue of a movie before its theatrical release is an important but challenging problem that requires a high level of Artificial Intelligence. Nowadays, social media has shown its predictive power in various domains, which motivates us to exploit social media content to predict box-office revenues. In this study, we employ both linear and non-linear regression models, which are based on the crowd wisdom of social media, especially the posts of users, to predict movie box-office revenues. More specifically, the attention and popularity of the movie, purchase intention of users, and comments of users are automatically mined from social media data. In our model, the use of Linear Regression and Support Vector Regression in predicting the box-office revenue of a movie before its theatrical release is explored. To evaluate the effectiveness of the proposed approach, a cross-validation experiment is conducted. The experimental results show that large-scale social media content is correlated with movie box-office revenues and that the purchase intention of users can lead to more accurate movie box-office revenue predictions. Both the linear and non-linear prediction models have the advantage of predicting movie grosses in our experiments.
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TL;DR: In this article, the authors review the impact that this explosion of data is having on product forecasting and how it is improving it, and explore how such data can be used to obtain insights into consumer behavior, and the impact of such data on organizational forecasting.
112 citations
TL;DR: New box-office forecasting models are presented to enhance the forecasting accuracy by utilizing review sentiments and employing non-linear machine learning algorithms, and an independent subspace method (ISM) is applied.
70 citations
Cites background from "Predicting movie Box-office revenue..."
...The second type is audience-related variables, which are opinions gathered from viewers who have watched or plan to watch a movie, such as viewer ratings [12, 18] or short text comments [5, 6, 16, 32, 33, 38]....
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TL;DR: The study results show that reviewer reputation, the disclosure of reviewer identity, and review depth positively affect the helpfulness of an online review, and the moderating effects of product type exist for these determinants on helpfulness.
Abstract: More and more people are gravitating to reading online product reviews prior to making purchasing decisions. Because a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate the various determinants of the helpfulness of reviews, and it also intends to examine the moderating effect of product type, that is, the experience or search goods in relation to the helpfulness of online reviews. The study results show that reviewer reputation, the disclosure of reviewer identity, and review depth positively affect the helpfulness of an online review. The moderating effects of product type exist for these determinants on helpfulness. That is, the number of reviews for a product and the disclosure of reviewer identity have a greater influence on the helpfulness for experience goods, while reviewer reputation, review extremity, and review depth are more important for helpfulness in relation to search goods. The interaction effects exist for average review rating and average review depth for a product with review helpfulness on product sales. The results of the study will identify helpful online reviews and assist in designing review sites effectively.
57 citations
Cites background from "Predicting movie Box-office revenue..."
...Recent research has also shown that customer reviews can have a positive influence on sales (Chevalier and Mayzlin 2006; Du, Xu, and Huang 2014; Ghose and Ipeirotis 2011; Kim, Hong, and Kang 2015; Kim, Park, and Park 2013; Liu et al. 2014; Rui, Liu, and Whinston 2013)....
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...Mudambi and Schuff (2010) suggested that review extremity, review depth, and product type affect the perceived helpfulness of the review where product type moderates the effect of review extremity on the helpfulness of the review....
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...Recent research has also shown that customer reviews can have a positive influence on sales (Chevalier and Mayzlin 2006; Du, Xu, and Huang 2014; Ghose and Ipeirotis 2011; Kim, Hong, and Kang 2015; Kim, Park, and Park 2013; Liu et al. 2014; Rui, Liu, and Whinston 2013)....
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TL;DR: In this article, the impact of review helpfulness on box office revenue was investigated. But, the authors focused on the review rating and review extremity of online customer reviews in the design of online sites for movies, rather than the number of reviews and review length.
Abstract: Purpose
While a number of studies examined the eWOM (online word-of-mouth) factors affecting box office, the studies on the impact of review helpfulness on box office are lacking. The purpose of this paper is to fill the void in previous studies and further extend prior work regarding eWOM and box office. In order to explain the interaction effect of helpfulness with other variables on product sales, this study posits that review characteristics such as number of reviews, review rating, review length interact with review helpfulness to have an influence on box office. Further, as the studies that have examined whether eWOM factors are significant in box office performances for the international markets other than US are lacking, this study is targeting Korean markets to validate the effect of eWOM on box office.
Design/methodology/approach
This study used publicly available data from www.naver.com to build a sample of online review data concerning box office. The final sample of the study included 2090 movies.
Findings
The results indicated that in cases when the review is helpful, the number of reviews and review length are more greatly influencing box office. Review rating, review extremity, and helpfulness for reviewer are important determinants for review helpfulness.
Practical implications
Managers can concentrate on the review rating and review extremity of online customer reviews in the design of online sites for movies. The design of user review systems can follow the direction that promotes more helpfulness for online user reviews based on an enhanced understanding of what drives helpfulness voting.
Originality/value
Given that previous studies on the effect of review helpfulness on box office are lacking, it contributes to eWOM literature by investigating the impact of review helpfulness on box office revenue.
50 citations
01 Dec 2017
TL;DR: A decision support system for movie investment sector using machine learning techniques that predicts an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Metacritic.
Abstract: Predicting society's reaction to a new product in the sense of popularity and adaption rate has become an emerging field of data analysis The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies is available over the internet This study proposes a decision support system for movie investment sector using machine learning techniques This research helps investors associated with this business for avoiding investment risks The system predicts an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Metacritic Using Support Vector Machine (SVM), Neural Network and Natural Language Processing the system predicts a movie box office profit based on some pre-released features and post-released features This paper shows Neural Network gives an accuracy of 841% for pre-released features and 8927% for all features while SVM has 8344% and 8887% accuracy for pre-released features and all features respectively when one away prediction is considered Moreover, we figure out that budget, IMDb votes and no of screens are the most important features which play a vital role while predicting a movie's box-office success
31 citations
Cites background from "Predicting movie Box-office revenue..."
...Some researchers made a prediction based on social media, social network and hype analysis where they calculated positivity of reviews and number of comments related to a particular movie [20-23]....
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01 Jul 1992
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
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11,211 citations