Predicting the performance of TV series through textual and network analysis: The case of Big Bang Theory


Autoři: Andrea Fronzetti Colladon aff001;  Maurizio Naldi aff002
Působiště autorů: Department of Engineering, University of Perugia, Via G. Durant, 93, 06125 Perugia, Italy aff001;  Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy aff002;  Department of Law, Economics, Politics and Modern languages, LUMSA University, Rome, Italy aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225306

Souhrn

TV series represent a growing sector of the entertainment industry. Being able to predict their performance allows a broadcasting network to better focus the high investment needed for their preparation. In this paper, we consider a well known TV series—The Big Bang Theory—to identify factors leading to its success. The factors considered are mostly related to the script, such as the characteristics of dialogues (e.g., length, language complexity, sentiment), while the performance is measured by the reviews submitted by viewers (namely the number of reviews as a measure of popularity and the viewers’ ratings as a measure of appreciation). Through correlation and regression analysis, two sets of predictors are identified respectively for appreciation and popularity. In particular the episode number, the percentage of male viewers, the language complexity and text length emerge as the best predictors for popularity, while again the percentage of male viewers and the language complexity plus the number of we-words and the concentration of dialogues are the best choice for appreciation.

Klíčová slova:

Computational linguistics – Economics – Language – Regression analysis – Semantics – Social networks – Origin of the universe


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Článek vyšel v časopise

PLOS One


2019 Číslo 11