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Movie Attendance Prediction

by Kushal Gevaria, Rijuta Wagh, Lynette D'Mello
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 3
Year of Publication: 2015
Authors: Kushal Gevaria, Rijuta Wagh, Lynette D'Mello
10.5120/ijca2015906939

Kushal Gevaria, Rijuta Wagh, Lynette D'Mello . Movie Attendance Prediction. International Journal of Computer Applications. 130, 3 ( November 2015), 14-17. DOI=10.5120/ijca2015906939

@article{ 10.5120/ijca2015906939,
author = { Kushal Gevaria, Rijuta Wagh, Lynette D'Mello },
title = { Movie Attendance Prediction },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 3 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number3/23188-2015906939/ },
doi = { 10.5120/ijca2015906939 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:24:02.060154+05:30
%A Kushal Gevaria
%A Rijuta Wagh
%A Lynette D'Mello
%T Movie Attendance Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 3
%P 14-17
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Posting online reviews and rating a movie is a very popular way to obtain information about movies. An online data set of reviews of the movies was taken from IMDB site. This paper uses two models to predict movie theatre capacity for the weekly released movies. The diffusion model, Sawhney and Eliashberg (1996) model predicts the capacity of movie theatre through time-to-decide and time-to-act parameters. The Hierarchical Bayes model consists of three models which are regression model, standard logit model and nested logit model and their efficiency is explained with detail. Finally, these two models are compared and their accuracy is determined.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Attendance Prediction Dynamic Prediction Autoregressive Model Diffusion Model Sentiment Analysis Data Mining Linear Regression Models Canibalization Hierarchical Bayesian Approach

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