Nerdiness and characterization in The Big Bang Theory: corpus-based analysis and scene-based analysis

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. 

Victim characterization in crime dramas: Mainly scene-based analysis with self-designed categorizations 

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. 

Sentimental based analysis of movie reviews

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.