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

PLoS One. 2019 Nov 21;14(11):e0225306. doi: 10.1371/journal.pone.0225306. eCollection 2019.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Humans
  • Internet
  • Models, Theoretical*
  • Social Media*
  • Television*
  • Text Messaging

Grants and funding

Work partially supported by MIUR, the Italian Ministry of Education, University and Research, under PRIN Project n. 20174LF3T8 AHeAD (Efficient Algorithms for HArnessing Networked Data).