Social networks such as YouTube have increased the ability to disseminate content in
different formats. Due to the narrative character that these contents have, automatically
identifying and extracting elements from the video can be a solution for the charac-
terization of narratives more effectively. This work presents the proposal to identify
characteristics of emotional aspects in audiovisual narratives, through the manipulation
of computational libraries and pre-trained machine learning models for the creation
of a Dataframe, containing the emotion information identified through detection and
facial recognition and recognition of facial expressions of characters found in videos.
From the results obtained, as a way of validating the proposal, a comparison was made
of the characteristics extracted manually by a professional in the communication area,
with the characteristics identified and extracted by the proposed architecture, which
showed the possibility of optimizing the process of extracting characteristics of YouTube
videos for the analysis of audiovisual narratives, through the automation of part of this
process.
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