Sentiment Analysis and Characterization of Sheldon Cooper in the Italian Dubbing of The Big Bang Theory: A GPT-Based Approach
Article Sidebar
Main Article Content
Abstract
This study examines the impact of dubbing on the portrayal of Sheldon Cooper in The Big Bang Theory, using sentiment analysis with OpenAI's GPT models. Through advanced natural language processing (NLP) techniques, it compares the emotional expressions in the English and Italian dialogues, highlighting key differences and similarities. The results suggest that sentiment analysis can significantly enhance the quality of dubbing, providing a more authentic and emotionally coherent representation of the character. This approach underscores the importance of preserving emotional nuances and the idiosyncratic peculiarities of characters in audiovisual translation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Proposed Policy for Journals That Offer Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).