Box-Office Revenue Estimation for Telugu Movie Industry using Predictive Analytic Techniques
V. Anantha Natarajan1, K SaiHarsha2, M Santhosh Kumar3
1V Anantha Natarajan, Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, (Andhra Pradesh), India.
2K Sai Harsha, Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, (Andhra Pradesh), India.
3M Santhosh Kumar, Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, (Andhra Pradesh), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 896-902 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2494037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The film industry is a business cloud for millions in investment and its multiple in revenue. Story rights, production costs, cast remunerations, film promotions etc. charges the production companies. This makes movie analytics inevitably essential for the success of a film and survival of the industry. From the sources like IMDB and Wikipedia; movie related information such as title, budget, synopsis of the story, genre, cast, release date etc. were collected. Analytics were performed on the related data for predicting movie premier collection share, first day share, first week share and overall gross collection to pre-determine the success of the film. Traditional machine learning algorithms and natural language processing techniques were collectively applied to make predictions. These estimations may aid production companies to forecast the make or break chances of the film prior to its release.
Keywords: Movie analytics, Machine learning algorithms, Natural language processing, IMDB, and Wikipedia
Scope of the Article: Data Analytics