Research and Development

Modeling and Predicting Audience Response


Entertainment Science: Data Analytics and Practical Theory for Movies, Games, Books, and Music by Thorsten Hennig-Thurau and Mark B. Houston, Leveraging Analytics to Produce Compelling and Profitable Film Content by Ronny Behrens, Natasha Z. Foutz, Michael Franklin, Jannis Funk, Fernanda Gutierrez-Navratil, Julian Hofmann and Ulrike Leibfried, Movie Success Prediction Using Historical and Current Data Mining by Partha Chakraborty, Md Z. Rahman and Saifur Rahman, Movie Success Prediction Using Data Mining by Javaria Ahmad, Prakash Duraisamy, Amr Yousef and Bill Buckles and Forecasting Financial Success of Hollywood Movies - A Comparative Analysis of Machine Learning Methods by Dursun Delen and Ramesh Sharda.

Audience Measurement and Analytics

Ratings and Audience Measurement by Philip M. Napoli and Ratings Analysis: Audience Measurement and Analytics by James Webster, Patricia Phalen and Lawrence Lichty.

Audience Modeling and Simulation

Supporting Human Creative Story Authoring with a Synthetic Audience by Brian O'Neill and Mark O. Riedl, Reader-model-based Story Generation by Peter Mawhorter, Cognitive Models of Discourse Comprehension for Narrative Generation by James Niehaus and R. Michael Young, Toward a Computational Model of Affective Responses to Stories for Augmenting Narrative Generation by Brian O'Neill, Affect and Narrative: A Model of Response to Stories by David S. Miall, A Computational Model of Emotional Response to Stories by Adam Fitzgerald, Gurlal Kahlon and Mark O. Riedl, Affect Simulation with Primary and Secondary Emotions by Christian Becker-Asano and Ipke Wachsmuth, Artificial Emotion Simulation Model by Valentin Lungu, Making Sense of Entertainment: On the Interplay of Emotion and Cognition in Entertainment Experience by Anne Bartsch and Mary Beth Oliver, Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation edited by Ron Sun, Behavioral Modeling and Simulation: From Individuals to Societies by Greg L. Zacharias, Jean E. Macmillan and Susan B. van Hemel, The History of Agent-based Modeling in the Social Sciences by Carl O. Retzlaff, Martina Ziefle and André C. Valdez, Agent-based Models in Empirical Social Research by Elizabeth Bruch and Jon Atwell and Using Empirical Data for Designing, Calibrating and Validating Simulation Models by Klaus G. Troitzsch.

Multi-agent Systems and Machine Learning

General Principles of Learning-based Multi-agent Systems by David H. Wolpert, Kevin R. Wheeler and Kagan Tumer, Learning in Multi-agent Systems by Eduardo Alonso, Mark d'Inverno, Daniel Kudenko, Michael Luck and Jason Noble, Multiagent Learning: Basics, Challenges, and Prospects by Karl Tuyls and Gerhard Weiss, A Survey of Learning in Multiagent Environments: Dealing with Non-stationarity by Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag and Enrique M. de Cote, Multi-agent Reinforcement Learning: A Selective Overview of Theories and Algorithms by Kaiqing Zhang, Zhuoran Yang and Tamer Başar and Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications by Thanh T. Nguyen, Ngoc D. Nguyen and Saeid Nahavandi.

Predictive Modeling and Analytics

Predictive Analytics: Modeling and Optimization edited by Vijay Kumar and Mangey Ram, Effective Predictive Analytics and Modeling Based on Historical Data by Sheikh M. Idrees, M. Afshar Alam, Parul Agarwal and Lubna Ansari, Predictive Analytics: A Survey, Trends, Applications, Opportunities & Challenges by Nishchol Mishra and Sanjay Silakari and Predictive Analytics in Motion Picture Industry with Use of Various Machine Learning Techniques by Piotr Renau.

Computational Narratology

Predicting Box Office from the Screenplay: An Empirical Model by Starling D. Hunter and Susan Smith, Predicting Box Office from the Screenplay: A Text Analytical Approach by Starling D. Hunter, Susan Smith and Saba Singh, Relating Entertainment Features in Screenplays to Movie Performance: An Empirical Investigation by Brianna J. Paulich and V. Kumar and Interpretable Screenplay Quality Assessment by Ming-Chang Chiu and Tiantian Feng.

Automatic Story Generation and Evaluation

Reader-model-based Story Generation by Peter Mawhorter, Predicting Reader Response in Narrative by Inderjeet Mani, Cognitive Models of Discourse Comprehension for Narrative Generation by James Niehaus and R. Michael Young, Toward a Computational Model of Affective Responses to Stories for Augmenting Narrative Generation by Brian O'Neill and STORYEVAL: An Empirical Evaluation Framework for Narrative Generation by Jonathan P. Rowe, Scott W. McQuiggan, Jennifer L. Robison, Derrick R. Marcey and James C. Lester.