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Improving Productivity in Hollywood with Data Science: Using Emotional Arcs of Movies to Drive Product and Service Innovation in Entertainment Industries.

Del Vecchio, Marco, Kharlamov, Alexander A., Parry, Glenn and Pogrebna, Ganna (2020) Improving Productivity in Hollywood with Data Science: Using Emotional Arcs of Movies to Drive Product and Service Innovation in Entertainment Industries. Journal of the Operational Research Society.

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Abstract

Improving productivity in the entertainment industry is a very challenging task as it heavily depends on generating attractive content for the consumers. The consumer-centric design (putting the consumers at the centre of the content development and production) focuses on ways in which businesses can design customized services and products which accurately reflect consumer preferences. We propose a new framework which allows to use data science to optimize content-generation in entertainment and test this framework for the motion picture industry. We use the natural language processing methodology combined with econometric analysis to explore whether and to what extent emotions shape consumer preferences for media and entertainment content, which, in turn, affect revenue streams. By analyzing 6,174 movie scripts, we generate the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are then plugged into an econometric model to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers’ and critics’ reviews. We find that emotional arcs in movies can be partitioned into 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This U-shaped emotional arc results in financially successful movies irrespective of genre and production budget. Implications of this analysis for generating on-demand content and improving productivity in entertainment industries are discussed.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Del Vecchio, Marco
Kharlamov, Alexander A.a.kharlamov@surrey.ac.uk
Parry, Glenng.parry@surrey.ac.uk
Pogrebna, Ganna
Date : 2 March 2020
Funders : EPSRC - Engineering and Physical Sciences Research Council
DOI : 10.1080/01605682.2019.1705194
Copyright Disclaimer : Copyright 2019 Taylor & Francis
Uncontrolled Keywords : Sentiment analysis; Sentiment mining; Productivity; Consumer-centric design; Entertainment; AI
Additional Information : The first version of this paper was presented at the research seminar at Warwick Manufacturing Group in July 2017 under the title “Sentiment-driven Consumer-centric Design: Understanding Emotional Trajectories of Films to Drive Business Model Innovation in Media and Entertainment Industries”. We are grateful to the participants at research seminars at the University College London, University of York, University of East Anglia, Impact Showcase at the Birmingham Business School, the Data Science in Entertainment Industries Conference in Manchester in March 2018, and the Google Catalyst Summit in Dublin in May 2018 for many useful comments and suggestions. We thank Alexander Milanovic for excellent research assistance.
Depositing User : Diane Maxfield
Date Deposited : 24 Jan 2020 12:09
Last Modified : 03 Mar 2020 10:52
URI: http://epubs.surrey.ac.uk/id/eprint/853392

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