Predicting Movie Prices Through Dynamic Social Network Analysis

Doshi, Lyric; Krauß, Jonas; Nann, Stefan; Gloor, Peter A.

doi:10.1016/j.sbspro.2010.04.052

Cited by 28 publications

(8 citation statements)

References 9 publications

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“…We created the classification labels based on the Rotten tomato scores that we crawled from Rotten Tomatoes' website with the Selenium 3 and Beautiful Soup python packages (Richardson, 2013). These scores serve as a credible indicator of a movie's success (Doshi et al, 2010). We classify movies following the Rotten Tomato rule; if the review score is greater than 75, the corresponding movie is classified fresh (1); if its score is less than 60, the movie is classified not fresh (0).…”

Section: Datamentioning

confidence: 99%

Proceedings of the Second Workshop on Storytelling

Ferraro¹,

Huang²,

Lukin³

2019

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Pictures can enrich storytelling experiences. We propose a framework that can automatically compose a picture book by understanding story text and visualizing it with painting elements, i.e., characters and backgrounds. For story understanding, we extract key information from a story on both sentence level and paragraph level, including characters, scenes and actions. These concepts are organized and visualized in a way that depicts the development of a story. We collect a set of Chinese stories for children and apply our approach to compose pictures for stories. Extensive experiments are conducted towards story event extraction for visualization to demonstrate the effectiveness of our method.

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Section: Datamentioning

confidence: 99%

Proceedings of the Second Workshop on Storytelling

Ferraro¹,

Huang²,

Lukin³

2019

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“…In some cases, success prediction of a movie were made through neural network analysis ( [7], [18]). Some researchers made prediction based on social media, social network and hype analysis ( [16], [17], [19], [20]) where they calculated positivity and number of comments related to a particular movie. Moreover few people had predicted Box Office movies' success based on Twitter tweets and YouTube comments.…”

Section: Literature Reviewsmentioning

confidence: 99%

A machine learning approach to predict movie box-office success

Quader

Gani

Chaki

et al. 2017

2017 20th International Conference of Computer and Information Technology (ICCIT)

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Making a prediction of 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 huge amount of data related to movies is available over the internet and that is why it is an interesting topic for data analysis. Machine learning is a novel approach for analyzing data. Our paper proposes a decision support system for movie investment sector using machine learning techniques. In that case, our system will help investors related with this business to avoid investment risks. The system will predict an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomato, Box Office Mojo and Meta Critic. Using different machine learning algorithms, Natural Language Processing and other techniques the system will predict a movie box office profit based on some features like who are the cast and director members, budget, movie release time, various types of movie rating, movie reviews and then process that data for classification.

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“…Doshi et al 49 use a combination of social network analysis and sentiment analysis to attempt to predict trends, focusing particularly on movie prices. By comparing buzz, represented by blog betweenness and other social network analysis metrics, sentiment metrics obtained from discussions in forums, and box offi ce performance data, they use multilinear and non-linear regression to attempt to predict fi nal box offi ce return.…”

Section: Travel Images On Facebookmentioning

confidence: 99%

Measuring the diffusion of marketing messages across a social network

Rogers

Chapman²,

Giotsas³

2012

J Direct Data Digit Mark Pract

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The last few years have seen signifi cant investment in social media as an adver sing, marke ng and customer outreach opportunity. In the US alone, in 2010, almost $ 1.7 bn was spent by adver sers on social media marke ng, with 53 per cent specifi cally allocated to Facebook 1 . Due to the explicit links that users maintain with each other, social media pla orms are perceived as a highly suited environment for network-based marke ng: word-of-mouth marke ng, diff usion of innova on, or buzz and viral marke ng 2 all aim to take advantage of the rela onships between users to facilitate the spread of awareness or adop on. In order to predetermine the eff ec veness of such campaigns, it is important to be able to es mate poten al return on investment. In par cular, the ability to model exis ng networks, track the propaga on of marke ng messages and es mate customer exposures and impressions are essen al for this purpose. A wide range of techniques to measure no ons such as user engagement on such pla orms have been developed and there also exists a signifi cant amount of research on modelling contagion and diff usion in network-based environments that can be exploited to generally refi ne an overall marke ng strategy. However, the structure and proper es of diff erent social media pla orms introduce various constraints on both the means via which data propagate and the visibility of content and nodes, constraints that must be taken into account when modelling or measuring the impact of social media campaigns. Perfect informa on about exposures within a given graph to a given message will not be available and as such it is important to inves gate and defi ne methodologies for diff usion monitoring that are suited to specifi c pla orms.

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Predicting Movie Prices Through Dynamic Social Network Analysis

Cited by 28 publications

References 9 publications

Proceedings of the Second Workshop on Storytelling

Proceedings of the Second Workshop on Storytelling

A machine learning approach to predict movie box-office success

Measuring the diffusion of marketing messages across a social network

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