Anand Bhave

Bio: Anand Bhave is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Big data. The author has an hindex of 2, co-authored 2 publications receiving 26 citations.

Role of different factors in predicting movie success

Abstract: Due to rapid digitization and emergence of social media the movie industry is growing by leaps and bounds. The average number of movies produced per year is greater than 1000. So to make the movie profitable, it becomes a matter of concern that the movie succeeds. Given the low success rate, models and mechanisms to predict reliably the ranking and or box office collections of a movie can help de-risk the business significantly and increase average returns. The current predictive models available are based on various factors for assessment of the movie. These include the classical factors such as cast, producer, director etc. or the social factors in form of response of the society on various online platforms. This methodology lacks to harvest the required accuracy level. Our paper suggests that the integration of both the classical and the social factors (anticipation and user feedback) and the study of interrelation among the classical factors will lead to more accuracy.

Relationships between classical factors, social factors and box office collections

Abstract: Every year the number of movie produced and released surpass the previous year's count and so do the total box office collections. So in this quality centric industry, it becomes imperative that the movie succeeds both in terms of box office collections and critical reviews and also renders profit. Due to advent of predictive analytics and big data generated through various social interactions, models to predict accurately the total gross of a movie can be devised, which eventually help the movie studio by giving constructive feedback both in pre-production and post-production phase. So the availability of this data gathered from various social platforms like IMDb, YouTube and Wikipedia can help to gauge the society's reaction and response towards a particular movie. It can also foretell a society's anticipation towards a particular movie. In this paper, we have built predictive models by establishing links between classical features, social media features and the overall success of the movie which includes total box office collection and the critics rating or review. The results show that the prediction model built using integration of classical as well as social factors can achieve higher accuracy rate.

Twitter Mining for Discovery, Prediction and Causality: Applications and Methodologies

Abstract: Twitter has found substantial use in a number of settings. For example, Twitter played a major role in the 'Arab Spring' and has been adopted by a large number of the Fortune 100. All of these and other events have led to a large database of Twitter tweets that has attracted the attention of a number of companies and researchers through what has become known as 'Twitter mining' also known as 'TwitterMining'. This paper analyses some of the approaches used to gather information and knowledge from Twitter for Twitter mining. In addition, this paper reviews a number of the applications that employ Twitter Mining, investigating Twitter information for prediction, discovery and as an informational basis of causation. Copyright © 2015 John Wiley & Sons, Ltd.

Using Crowd-Source Based Features from Social Media and Conventional Features to Predict the Movies Popularity

Abstract: Predicting the success of movies has been of interest to economists and investors (media and production houses) as well as predictive analysts. A number of attributes such as cast, genre, budget, production house, PG rating affect the popularity of a movie. Social media such as Twitter, YouTube etc. are major platforms where people can share their views about the movies. This paper describes experiments in predictive analysis using machine learning algorithms on both conventional features, collected from movies databases on Web as well as social media features (text comments on YouTube, Tweets). The results demonstrate that the sentiments harnessed from social media and other social media features can predict the success with more accuracy than that of using conventional features. We achieved best value of 77% and 61% using selected social media features for Rating and Income prediction respectively, whereas selected conventional features gave results of 76.2% and 52% respectively. More it was found that the blend of both types of attributes (conventional and those collected from social media) can outperform the existing approaches in this domain.

An inside look into the complexity of box-office revenue prediction in China:

Abstract: In this article, we discuss various elements contributing to exerting influence on box office in China, which are divided into internal and external factors. Since these factors could merely be quantified by online data sources partially or inaccurately, we propose that relativity analysis is more reasonable than precise revenue prediction. Trailer is selected as the combination of movie content and online behavior prior to releasing. Indexes from seven mainstream video websites are retrieved by the designed big data system which is integrated with the Internet of things technology. Correlation coefficients of different time periods are calculated. We apply multiple linear regression with stepwise method in modeling and prove that watching counts of 1 week before releasing on Youku is the barometer of market performance, especially the first week revenues. We also manifest the power of influential users through constructing Sina Weibo acquisition and analysis system.