Applying Automatic Text Analysis Methodologies to Audiovisual Serial Product
Keywords:TV series, Text analysis, Twitter, Sentiment analysis, Servant
The goal of this paper is to explore the application of automatic text analysis methodologies to contemporary audiovisual serial narratives. As a case study we use the Apple TV+ series Servant (Apple TV+, 2019-). We first focused on the primary text (the English dialogue used in the TV series) to examine the role of the dialogue and the characters’ interactions through an exploratory application of Social Network Analysis. In particular, tagging the dialogue in XML format allowed the identification and quantification of scenes, characters and speaker-receiver pairs that were used to implement Social Network Analysis. Secondly, we collected tweets as the secondary text (the text produced by the Twitter audience), and analysed users’ behaviours and preferences considering both semantic and quantitative points of view. We underlined how analyses conducted on tweet sentiment can help to monitor this social engagement mechanism and how it may evolve over time. The paper is highly experimental in that, in addition to findings related to the narrative structure of the serial product (thanks to the primary text) and analysis of the relationship over time with the audience (thanks to the secondary text), it aims to test a shared analytical framework that can enable large-scale comparative investigations of contemporary TV series.
How to Cite
Copyright (c) 2022 Marta Rocchi
This work is licensed under a Creative Commons Attribution 4.0 International License.