Author:
(1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction
Dataset
Methods
Results
Discussion
Conclusion, References, and Appendix Abstract—In this paper, we analyze the character networks extracted from three popular television series and explore the relationship between a TV show episode’s character network metrics and its review from IMDB. Character networks are graphs created from the plot of a TV show that represents the interactions of characters in scenes, indicating the presence of a connection between them. We calculate various network metrics for each episode, such as node degree and graph density, and use these metrics to explore the potential relationship between network metrics and TV series reviews from IMDB. Our results show that certain network metrics of character interactions in episodes have a strong correlation with the review score of TV series. Our research aims to provide more quantitative information that can help TV producers understand how to adjust the character dynamics of future episodes to appeal to their audience. By understanding the impact of character interactions on audience engagement and enjoyment, producers can make informed decisions about the development of their shows. Index Terms—computational linguistics, character networks, network analysis, TV series INTRODUCTION People only have so much time. When we come home from work or school, we have our priorities; we do chores, send emails, or catch up on our favorite TV shows. For producers and TV channels, this time is imperative. The number of viewers on a show can vastly differ based on the time a show airs, and there are only so many slots available; ideal “prime times” like Tuesday at 9 pm can only be occupied by so many shows. Additionally, viewers only have so many shows they can be invested in at the same time. For the 122.4 million households that watch TV, this poses an interesting question. What makes a show good; more so, what makes a show continue to be good? Unlike movies, TV shows consistently need to produce episode after episode, season after season. Sometimes viewership drops, and your favorite 9 o’clock show is canceled and taken off-air, or disappears from your favorite streaming service. But why? For the executives producing a show, the answer is simple: ratings. High ratings mean more seasons, and low ratings mean the show ends. But there’s more to it than that. Quality fluctuates, and many shows are notorious for their lackluster attempts at good content in later seasons. What causes a great show, loved and revered in seasons one through three, to suddenly become an insult to its fans, its ratings plummeting in the next four seasons? To answer this question, we need to look at what makes a show good in the first place. Ratings are determined by viewer satisfaction, so what did I see in a show that made me want to invest my time in the first place? Is that related to why I don’t enjoy it now? The answer could be something that the viewers themselves might not even know. In this paper, we attempt to understand the psychology behind these ratings through character networks, and the different interactions between characters. More specifically, we try to find a correlation between the reviews of TV show episodes and the character interactions. We use different metrics of network analysis to quantitatively answer the question: do character interactions affect ratings in TV series? Network analysis is described as the “set of integrated techniques to depict relations among actors and to analyze the social structures that emerge from the recurrence of these relations”. We use network analysis to analyze graphs. A graph is a collection of nodes (vertices) along with identified pairs of nodes connected with edges. This paper applies network analysis to specific graphs called character networks, where character networks are defined as graphs that represent the interactions between characters in a fictional setting. Different methodologies to observe the behavior of a network and perform network analysis are known as network metrics. We can thus transform our previous question into: for a given TV show, is there a connection between a network metric and the popularity of an episode? This paper is available on arxiv under CC 4.0 license. Author: (1) Melody Yu, Sage Hill School, California, USA. Author: Author: (1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction Dataset Methods Results Discussion Conclusion, References, and Appendix Abstract and Introduction Abstract and Introduction Dataset Dataset Methods Methods Results Results Discussion Discussion Conclusion, References, and Appendix Conclusion, References, and Appendix Abstract—In this paper, we analyze the character networks extracted from three popular television series and explore the relationship between a TV show episode’s character network metrics and its review from IMDB. Character networks are graphs created from the plot of a TV show that represents the interactions of characters in scenes, indicating the presence of a connection between them. We calculate various network metrics for each episode, such as node degree and graph density, and use these metrics to explore the potential relationship between network metrics and TV series reviews from IMDB. Our results show that certain network metrics of character interactions in episodes have a strong correlation with the review score of TV series. Our research aims to provide more quantitative information that can help TV producers understand how to adjust the character dynamics of future episodes to appeal to their audience. By understanding the impact of character interactions on audience engagement and enjoyment, producers can make informed decisions about the development of their shows. Abstract —In this paper, we analyze the character networks extracted from three popular television series and explore the relationship between a TV show episode’s character network metrics and its review from IMDB. Character networks are graphs created from the plot of a TV show that represents the interactions of characters in scenes, indicating the presence of a connection between them. We calculate various network metrics for each episode, such as node degree and graph density, and use these metrics to explore the potential relationship between network metrics and TV series reviews from IMDB. Our results show that certain network metrics of character interactions in episodes have a strong correlation with the review score of TV series. Our research aims to provide more quantitative information that can help TV producers understand how to adjust the character dynamics of future episodes to appeal to their audience. By understanding the impact of character interactions on audience engagement and enjoyment, producers can make informed decisions about the development of their shows. Abstract Index Terms—computational linguistics, character networks, network analysis, TV series Index Terms —computational linguistics, character networks, network analysis, TV series Index Terms INTRODUCTION People only have so much time. When we come home from work or school, we have our priorities; we do chores, send emails, or catch up on our favorite TV shows. For producers and TV channels, this time is imperative. The number of viewers on a show can vastly differ based on the time a show airs, and there are only so many slots available; ideal “prime times” like Tuesday at 9 pm can only be occupied by so many shows. Additionally, viewers only have so many shows they can be invested in at the same time. For the 122.4 million households that watch TV, this poses an interesting question. What makes a show good; more so, what makes a show continue to be good? Unlike movies, TV shows consistently need to produce episode after episode, season after season. Sometimes viewership drops, and your favorite 9 o’clock show is canceled and taken off-air, or disappears from your favorite streaming service. But why? For the executives producing a show, the answer is simple: ratings. High ratings mean more seasons, and low ratings mean the show ends. But there’s more to it than that. Quality fluctuates, and many shows are notorious for their lackluster attempts at good content in later seasons. What causes a great show, loved and revered in seasons one through three, to suddenly become an insult to its fans, its ratings plummeting in the next four seasons? To answer this question, we need to look at what makes a show good in the first place. Ratings are determined by viewer satisfaction, so what did I see in a show that made me want to invest my time in the first place? Is that related to why I don’t enjoy it now? The answer could be something that the viewers themselves might not even know. In this paper, we attempt to understand the psychology behind these ratings through character networks, and the different interactions between characters. More specifically, we try to find a correlation between the reviews of TV show episodes and the character interactions. We use different metrics of network analysis to quantitatively answer the question: do character interactions affect ratings in TV series? Network analysis is described as the “set of integrated techniques to depict relations among actors and to analyze the social structures that emerge from the recurrence of these relations”. We use network analysis to analyze graphs. A graph is a collection of nodes (vertices) along with identified pairs of nodes connected with edges. This paper applies network analysis to specific graphs called character networks, where character networks are defined as graphs that represent the interactions between characters in a fictional setting. Different methodologies to observe the behavior of a network and perform network analysis are known as network metrics. We can thus transform our previous question into: for a given TV show, is there a connection between a network metric and the popularity of an episode? This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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