Quantitative study of the movie industry based on IMDb data

Big Data Analytics is an emerging business capability that is providing far more intelligence to the companies nowadays to make well-informed decisions and better formulate their business strategies. This has been made possible due to easy accessibility of immense volume of data stored in clouds in a secure manner. As a result, online product review platforms have also gained enormous popularity and are successfully providing various services to the consumers primarily via user-generated content. The thesis makes use of raw and unstructured data available on IMDB website, cleans it up and organizes it in a structured format suitable for quick analysis by various analytical softwares. The thesis then examines the available literature on analytics done on IMDB movie dataset and identifies that little work has been carried out in predicting the financial success of the movies. The thesis thus carries out data analytics on the IMDB movie sets and highlights several parameters like movie interconnectedness and director's credentials, which correlates positively with the movie gross revenue. The thesis thereafter loosely defines a movie innovative index encompassing of parameters like number of references, number of follows and number of remake and discusses how the abundance of some of these parameters have a positive impact on box office success of the movie. Contrarily the lack of presence of these parameters thereby characterizing an innovative movie may not be so well received by the audiences thus leading to poor box office performance. The thesis also proposes how the director's credentials in the film industry measured by his/her total number of nominations and awards winning in the Oscar have a positive impact on the financial success of the movie and their own career advancement.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Almadi, Kanika

Advisor dc:contributor.advisor

• T. Tony Ke and Clair Z.Q. Yang

• Engineering and Management Program.
• Integrated Design and Management Program.
• System Design and Management Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/113502

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/113502