This part presents a literature review regarding research studying characterization in TV series. The literature review not only provides an overview of current research on TV series' characterization, but also sheds light on our project, by suggesting possible analysis methods that are conducive to a comprehensive, inclusive, objective analysis of our own project.
In Bednarek's research (2012), the characterization in TBBT is analyzed, with a focus on the nerdiness features displayed by one of the main characters, Sheldon. In order to analyze Sheldon's nerdiness in great detail, she adopts two approaches. On the one hand, she builds a Sheldon-dialogue corpus, and reviews it from a linguistic perspective, by analyzing some keywords and concordances that may suggest Sheldon's nerdiness. On the other hand, she also closely reads the scenes in series 1, because there are some implicit cues also indicative of Sheldon's nerdiness, yet can be easily missed when adopting a general corpus analysis.
In addition, she also pays attention to the audience's reviews, because she believes that the audience can interact with the characters in various ways, such as predicting or interpreting the events, identifying themselves with the characters, liking/disliking the characters, etc. However, although Bednarek shrewdly notices the importance of reviews from the audience and defines "nerdiness" from the audience's perspective, she does not include a detailed study of audience's review of TBBT.
Rader and Rhineberger-Dunn (2010) delve into the characterization of female victims of crimes in several television crime dramas. They mainly adopt the close-reading strategy. They first define four crimes of interest and select episodes with crimes that can fall into any one of them. Then, they start an Excel spreadsheet in which the basic information about the victims and the criminal-victim relationship is presented in an organized manner. The clear categorization and the organized presentation will be adopted in our scene-based analysis of the characters' neediness in TBBT.
Similar to Bednarek, Rader and Rhineberger-Dunn also acknowledge the connection between the characterization and the audience review. To be more specific, they posit that the characterization of victims can significantly affect audience's understanding of the victims and crimes as a whole, given that the majority of audience have no previous experiences of crimes. However, they did not examine audience reviews closely either.
There is sparse research on sentimental analysis in TV series. Since movies and TV series are two similar genres, we examine previous studies on the sentimental analysis of movies. Chirgaiya et al. (2021) use machine learning techniques to conduct a sentimental analysis of movie reviews and prove that they can be accurate. Therefore, we would like to adopt a similar approach when analyzing reviews of the Big Bang Theory from the audience.
Lee (2016) pays attention to audience laughter and the humorous effect in The Big Bang Theory. Lee analyzes the humorous effect from a linguistic perspective and argues that the breach of the conversation norms in dialogues is an important attribute.
Lasekan and Mendez-Alarcon (2022) analyze the characterization of a servant teacher in Rita TV series. They define some attributes of a servant teacher persona and have a close examination of the scenes to see the attributes that can fit.
These two articles further prove that perceiving characterization through an in-depth scene-based analysis, either with a perspective of linguistic features or with a self-determined categorization system, can be insightful.
In light of the above research, characterization in TV series is worth delving into, as it often facilitates the connection between TV series scripts and audience reviews. In previous research, corpus-based linguistic analysis of scripts, manual in-depth scene-based analysis with a self-designed categorization system, and sentimental analysis tools have been adopted. Yet, none of the research has combined all these tools together, whereas a corpus of audience reviews has been seldom addressed or studied with a close reading approach.