Classification and Forecasting of Bollywood Movies by Commercial Success using Back-Propagation Neural Network model
Partha Shankar Nayak

Partha Shankar Nayak, PSN Academy, Bhadreswar, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 3600-3607 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7702129219/2019©BEIESP | DOI: 10.35940/ijitee.B7702.129219
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Abstract: Forecasting commercial success of motion pictures remained challenging for producers, critics and other industry leaders in this changing world of web and online media. In this study, the author has explored a back-propagation neural network model with 23 numeric input (BPNN-N23) for classification of Bollywood movies released during the years 2014 through 2017. The proposed model classifies movies in three classes namely “HIT”, “AVERAGE” and “FLOP”. Common procedures like data filtering, data cleaning and data normalization have been followed prior to feeding those data to the neural network. After comparing the performance of the proposed model with the benchmark models and works, the results show that the said model shows performance that is comparable to the published ones with respect to the assumed Indian empirical settings. This research reveals the extent of the effects and roles of the considered factors as well as the proposed model in predicting the fate of a Bollywood movie in India. 
Keywords: Artificial Neural Network, Back-propagation, Movie Review, Sentiment Analysis, Bollywood, SMOTE.
Scope of the Article: Classification