Animating evaluation: A multimodal appraisal analysis of story-time animation

Harvey, Gemma (2022). Animating evaluation: A multimodal appraisal analysis of story-time animation. University of Birmingham. M.A.

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Abstract

This dissertation presents the multimodal appraisal analysis of story-time animation videos; a form of computer-mediated motion picture entertainment that are conventionally uploaded to the social media platform YouTube. By means of the multimodal appraisal analysis, this dissertation aims to elucidate the potential of story-time animation to construe emotions and opinions. To achieve this goal, the dissertation establishes a multimodal appraisal methodology which facilitates the identification, description and analysis of emotions and opinions in texts which implement several communicative modes. The dissertation first outlines several linguistic frameworks constructed to identify emotions and opinions - such as Martin and White’s Appraisal Framework (2005) - and proceeds to explore how such frameworks have been adapted to identify emotions and opinions in multimodal texts. It then establishes the multimodal appraisal methodology established in this dissertation, designed specifically with animated media in mind. The dissertation then applies this multimodal appraisal framework to various scenes of three story-time animation videos produced by the YouTube content creator ‘Jaiden Animations’ in order to elucidate the evaluative potential of story-time animations. The multimodal appraisal analysis of this dissertation ascertained that story-time animations possess great potential to construe emotion and opinions however each of the communicative modes that it utilises commits to these emotions and opinions to varying degrees. It also observed that these communicative modes would complement and contradict one another in order to upscale and downscale emotions and opinions and to construe a wide range of emotion hybrids.

Type of Work:

Thesis (Masters by Research > M.A.)

Award Type:

Masters by Research > M.A.

Supervisor(s):